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41 results about "Regularized least squares" patented technology
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Regularized least squares (RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the resulting solution. RLS is used for two main reasons. The first comes up when the number of variables in the linear system exceeds the number of observations. In such settings, the ordinary least-squares problem is ill-posed and is therefore impossible to fit because the associated optimization problem has infinitely many solutions. RLS allows the introduction of further constraints that uniquely determine the solution.
Method and System for Communicating Multimedia Using Reconfigurable Rateless Codes and Decoding In-Process Status Feedback
ActiveUS20130308700A1Avoid erroneous decodingImprove performancePicture reproducers using cathode ray tubesCode conversionRaptor codeRegularized least squares
A method and system use capacity-approaching rateless code to communicate multimedia data even with very short codewords, such as 64 bits or less, via erasure and noise channels. The method provides a way to design the edge degree distribution of rateless codes for any arbitrary channel. Based on an equivalent metric of decoding behavior in any channels, the degree distribution of a rateless code such as Luby-transform codes and raptor codes is optimized based on in-process status of decoding. A regularized least-squares optimization is used to avoid erroneous decoding. Multiple feedbacks can further improve the performance.
Owner:MITSUBISHI ELECTRIC RES LAB INC
Discriminating speech and non-speech with regularized least squares
ActiveUS20070073538A1Digital computer detailsCharacter and pattern recognitionNoise classificationHigh dimensional
Sound discrimination techniques are disclosed that can be employed, for example, in the task of discriminating speech and non-speech in a noisy environment and other noise classification applications. In one particular embodiment, a classifier system is provided that includes a linear Regularized Least Squares classifier used directly on a high-dimensional cortical representation of the target sound. The regularization constant lambda (λ) can be selected automatically, yielding a parameter-free learning system. In addition, the high-dimensional hyperplane can be viewed directly in the cortical space, leading to greater interpretability of the classifier results.
Owner:HONDA MOTOR CO LTD
Nonlinear frequency modulation component decomposition-based time-frequency domain modal parameter identification method
ActiveCN107329932AEasy to identifyEasy to useComplex mathematical operationsDecompositionFrequency modulation
The invention discloses a nonlinear frequency modulation component decomposition-based time-frequency domain modal parameter identification method comprising steps of (1) acquiring a dynamic response signal of a to-be-identified structure and setting sampling time and frequency, (2) building a redundancy Fourier model including the response signal, instantaneous frequency and instantaneous amplitude via Fourier series, (3) extracting instantaneous frequency information of the response signal according to generalized parameterization time-frequency transform, (4) extracting instantaneous amplitude information of the response signal according to a regularized least square method according to the acquired instantaneous frequency information, (5) conducting structure modal parameter identification via a linear least square fitting algorithm based on the instantaneous frequency information and slope information of the instantaneous amplitude logarithm and (6) analyzing errors of an identification result. Signal analysis and modal parameter identification are conducted according to a structure vibration response signal, so simple and easy use can be achieved; modal parameter identification precision for high-compact modal structures can be effectively improved; and strong adaptability and interference resistance capacity can be provided.
Owner:SHANGHAI JIAO TONG UNIV
Laplace regularization least square synthetic aperture radar automatic target recognition method
InactiveCN101196564AImprove recognition rateImprove robustnessCharacter and pattern recognitionRadio wave reradiation/reflectionPaired DataFeature extraction
The invention discloses an automatic target recognition method of Laplace regularization least square synthetic aperture radar, which relates to radar technology field and aims at improving the discrimination of SAR target image by adopting the method and get a better robustness to directions. The implementation steps in the invention are: firstly to make feature extraction for all the samples in MSTAR database by adopting KPCA, take all the data in training sets as samples with signs, and use the data in training sets as samples without signs to set up a weighted undigraph Gequals to (V, E), see the data point as the peak V of G, then define the similarity of the paired data points as the side of the graph, calculate the Laplacian of the graph and take as a regular term and added to the regularization least square and call as Laplace regularization least square to calculate the relative optimization problem. Then, classify the sample without signs by classifying function gain from exercises. The method can solve the identification problem based on the two-dimension SAR image.
Owner:XIDIAN UNIV
Fully Bayesian Linear Regression
ActiveUS20080162386A1Prevent overfittingDigital computer detailsDigital dataLinear regressionHyperparameter
A simple yet powerful Bayesian model of linear regression is disclosed for methods and systems of machine learning. Unlike previous treatments that have either considered finding hyperparameters through maximum likelihood or have used a simple prior that makes the computation tractable but can lead to overfitting in high dimensions, the disclosed methods use a combination of linear algebra and numerical integration to work a full posterior over hyperparameters in a model with a prior that naturally avoids overfitting. The resulting algorithm is efficient enough to be practically useful. The approach can be viewed as a fully Bayesian version of the discriminative regularized least squares algorithm.
Owner:HONDA MOTOR CO LTD
Regularized least squares classification/regression
Techniques are disclosed that implement algorithms for rapidly finding the leave-one-out (LOO) error for regularized least squares (RLS) problems over a large number of values of the regularization parameter λ. Algorithms implementing the techniques use approximately the same time and space as training a single regularized least squares classifier / regression algorithm. The techniques include a classification / regression process suitable for moderate sized datasets, based on an eigendecomposition of the unregularized kernel matrix. This process is applied to a number of benchmark datasets, to show empirically that accurate classification / regression can be performed using a Gaussian kernel with surprisingly large values of the bandwidth parameter σ. It is further demonstrated how to exploit this large σ regime to obtain a linear-time algorithm, suitable for large datasets, that computes LOO values and sweeps over λ.
Owner:HONDA MOTOR CO LTD
Method and System for Through-the-Wall Imaging using Sparse Inversion for Blind Multi-Path Elimination
ActiveUS20150022390A1Suppress artifactsCompensation DistortionMulti-channel direction-finding systems using radio wavesRadio wave reradiation/reflectionImpulse responseMulti path
Targets are detected in a scene behind a wall by first transmitting a pulse through the wall. Then, a primary impulse response is detected by a sparse regularized least squares inversion applied to received signals corresponding to the reflected pulse. A delay operator that matches the primary impulse response to similar impulse responses in the received signals is also determined. A distortion of the pulse after the pulse passes through the wall but before the pulse is reflected by the target can also be determined. The distortion is used in an iterative process to refine the detection of the target and to suppress ghosting artifacts.
Owner:MITSUBISHI ELECTRIC RES LAB INC
Broadband compressed spectrum sensing method based on sub-band matching selection
ActiveCN105610525AReduce the number of iterationsReduce spectrum reconstruction timeTransmission monitoringFrequency spectrumAnalog signal
The invention discloses a broadband compressed spectrum sensing method based on sub-band matching selection. In a spectrum sensing stage, a secondary user carries out compressed sampling to received analog signals through an analog information converter, thus obtaining few compressed sampling data; frequency domain sparsity signals corresponding to the signals obtained by carrying out Nyquist sampling to the analog signals are reconstructed through an iteration mode; then the secondary user judges the occupation state of each sub-band on the reconstructed frequency domain sparsity signals by an energy detection method; the method has the advantages that the single frequency point matching selection is changed into the sub-band matching selection; the sub-bands of a primary user can be determined; the iterations of the algorithms in the spectrum reconstruction can be greatly reduced; the spectrum reconstruction time is shortened; the residual errors are updated by a regularized least square method; therefore, the spectrum reconstruction accuracy and robustness can be further improved; and the broadband spectrum sensing performance is improved.
Owner:NINGBO UNIV
System and method for rapid face recognition
A face recognition method is provided to use sparse representation and regularized least squares-based classification on a computing device. The method includes obtaining an image to be recognized as a test sample y and a set of training images of certain subjects as training sample matrix T, obtaining a sparse representation of the test sample and the training samples including an initial estimation of a sparse vector a, and constructing a new face dictionary comprising training samples with non-zero corresponding coefficients in the sparse vector a for the initial estimation. The method also includes obtaining new coefficients by solving a regularized least squares problem based on the constructed new face dictionary, and determining a face identity of the test sample based on minimum class residual calculated by using the new coefficients.
Owner:TCL CORPORATION
System and method for rapid face recognition
A face recognition method is provided to use sparse representation and regularized least squares-based classification on a computing device. The method includes obtaining an image to be recognized as a test sample y and a set of training images of certain subjects as training sample matrix T, obtaining a sparse representation of the test sample and the training samples including an initial estimation of a sparse vector a, and constructing a new face dictionary comprising training samples with non-zero corresponding coefficients in the sparse vector a for the initial estimation. The method also includes obtaining new coefficients by solving a regularized least squares problem based on the constructed new face dictionary, and determining a face identity of the test sample based on minimum class residual calculated by using the new coefficients.
Owner:TCL CORPORATION
Fully bayesian linear regression
ActiveUS7565334B2Prevent overfittingDigital computer detailsDigital dataHyperparameterComputer science
A simple yet powerful Bayesian model of linear regression is disclosed for methods and systems of machine learning. Unlike previous treatments that have either considered finding hyperparameters through maximum likelihood or have used a simple prior that makes the computation tractable but can lead to overfitting in high dimensions, the disclosed methods use a combination of linear algebra and numerical integration to work a full posterior over hyperparameters in a model with a prior that naturally avoids overfitting. The resulting algorithm is efficient enough to be practically useful. The approach can be viewed as a fully Bayesian version of the discriminative regularized least squares algorithm.
Owner:HONDA MOTOR CO LTD
QRS wave starting point end point positioning method based on regularized least square regression learning
ActiveCN106063704AImprove applicabilityImprove accuracyDiagnostic recording/measuringSensorsEcg signalAlgorithm
A QRS wave starting point end point positioning method based on regularized least square regression learning comprises the following steps: building a series of detection methods, detection criterions and threshold parameters according to function quadrature decomposition idea in the Hilbert space and the regularized least square regression learning algorithm, thus finally detecting the QRS wave and positioning the starting point and end point; carrying out inner product operation on an electrocardiosignal and a gauss function first order derived function so as to obtain an inner product sequence; selecting the reproducing kernel Hilbert space formed by the gauss kernel function as the approximation space of the regularized least square regression learning algorithm; using a real symmetric matrix square root decomposition method solving algorithm to obtain the novel inner product sequence.
Owner:ZHEJIANG HELOWIN MEDICAL TECH
Power equipment fault monitoring method based on mutual reconstruction single-class auto-encoder
ActiveCN112381180ATo achieve the purpose of abnormal detectionEssential feature representationData processing applicationsElectrical testingAlgorithmAnomaly detection
The invention discloses a power equipment fault monitoring method based on a mutual reconstruction single-class auto-encoder.The method comprises the following steps: preprocessing acquired magnetic field information of power equipment in normal operation to obtain a training data sample set, training K mutual reconstruction single-class random auto-encoders WSI-GAE by taking the training data sample set as input to obtain a final encoding result, performing single classification model training by using regularized least square single classification loss, obtaining a fitting error of each trained data sample, and selecting a threshold from fitting error sequences of the data samples arranged from large to small, for newly collected magnetic field information data of the power equipment, the obtained fitting error is compared with a threshold value, and when the fitting error is larger than the threshold value, it can be judged that the power equipment has abnormal conditions such as faults. According to the invention, a single-class classifier technology is used to realize anomaly detection, and the method is more suitable for the target of the invention. The fault abnormity monitoring accuracy of the power equipment is improved.
Owner:杭州拓深科技有限公司
Adaptive sparse-tree structure noise reduction method of very noisy vibration signal of main reducer
ActiveCN108844617APreserve the fault signaturePreserve the shock featureSubsonic/sonic/ultrasonic wave measurementWeight adjustmentReduction drive
The invention discloses an adaptive sparse-tree structure noise reduction method of a very noisy vibration signal of a main reducer. The method comprises a first phase in which position points relatedto a significant feature are detected from signals, and serve as basis of adaptive regular weight adjustment, and a wavelet coefficient related to the signal feature is reserved; and a second phase in which a tree-structure wavelet coefficient is estimated on the basis of the adaptive weight. The correlation structure between wavelet coefficients is used, the tree structure sparsity of noise reduction estimation can be improved by regular least square regression, noise components can be filtered from the very noisy vibration signal, the frequency modulated component related to a fault featureis reserved, and effective characteristic information in the signals is avoided from smooth transition.
Owner:WUHAN UNIV OF TECH
Semi-supervised classification method capable of simultaneously learning affinity matrix and Laplacian regularized least square
InactiveCN108805155AImprove performanceEasy to handleCharacter and pattern recognitionCoordinate descent methodClassification methods
The invention discloses a semi-supervised classification method capable of simultaneously learning an affinity matrix and a Laplacian regularized least square, which mainly comprises the following steps: firstly, a joint model capable of simultaneously learning the affinity matrix and the Laplacian regularized least square is established according to a training sample; secondly, the block coordinate descent method is used to optimize all kinds of variables in the model; and finally, the soft label of the sample is obtained by a Laplacian regularized least square classifier, and the dimension with the largest element in a label vector is selected as the category of the sample. The invention effectively fuses the sparse self-representation problem of samples and the Laplacian regularized least square classifier, and realizes the simultaneous optimization and mutual improvement of the sample affinity matrix and the Laplacian regularized least square classifier in the learning process. Theinvention has an explicit classifier function, so that the problem of an external sample can be effectively handled. Compared with other semi-supervised classification methods, the method has more accurate classification accuracy and good application prospects.
Owner:温州大学苍南研究院
Through-wall radar imaging method based on wall low-rank sparse constraint
PendingCN111551928AQuality improvementRealize recovery and reconstructionCharacter and pattern recognitionComplex mathematical operationsNuclear norm regularizationSparse constraint
The invention discloses a through-wall radar imaging method based on wall low-rank sparse constraint. Two important characteristics of signals in through-wall radar imaging are utilized; the wall reflects the low rank of clutters and the sparsity of target reflection signals are utilized; according to the invention, a wall clutter suppression and target reconstruction problem is converted into a regularization least square optimization problem; an objective function of the method comprises a data fidelity (least square) item, a nuclear norm regularization item and an l1 norm penalty item, integrated processing of wall clutter suppression and target imaging is achieved, wall clutters are separated out in an iterative loop, meanwhile, target signals are reconstructed and imaged, and the imaging quality is improved.
Owner:XI'AN PETROLEUM UNIVERSITY
Image compression method based on active learning and semi-supervised learning
InactiveCN102622771AShorten the timeIncrease the compression ratioImage codingImage segmentationImage compression
The invention discloses an image compression method based on active learning and semi-supervised learning, which selects color information of some representative pixel points in an image to learn a model which can predict colors of the remaining pixel points in the image on the basis of the active learning. The image compression method mainly includes the following steps that all pixel points of the image are provided, the image is segmented into a plurality of rectangular areas simply, one pixel point is respectively and randomly selected from each rectangular area, the pixel points are learned actively so as to select the most representative pixel points, grey level values and color values of the most representative pixel points are recorded, grey level values of the remaining pixel points of the image are only recorded, and a compression process is completed. During decompression, Laplace regularized least squares (LapRLS) serving as a semi-supervised learning algorithm is used for predicting and restoring colors of all pixel points.
Owner:ZHEJIANG UNIV
Regularized least squares classification or regression with leave-one-out (LOO) error
Techniques are disclosed that implement algorithms for rapidly finding the leave-one-out (LOO) error for regularized least squares (RLS) problems over a large number of values of the regularization parameter λ. Algorithms implementing the techniques use approximately the same time and space as training a single regularized least squares classifier / regression algorithm. The techniques include a classification / regression process suitable for moderate sized datasets, based on an eigendecomposition of the unregularized kernel matrix. This process is applied to a number of benchmark datasets, to show empirically that accurate classification / regression can be performed using a Gaussian kernel with surprisingly large values of the bandwidth parameter σ. It is further demonstrated how to exploit this large σ regime to obtain a linear-time algorithm, suitable for large datasets, that computes LOO values and sweeps over λ.
Owner:HONDA MOTOR CO LTD
Gamma radiation source item inversion method and system based on regularized least square method
The invention discloses a gamma radiation source item inversion method and system based on a regularized least square method. The method comprises the following steps of S1, carrying out area divisionon a gamma radiation source item, wherein radioactivity in each source item division area is uniformly distributed in a space; S2, according to dose rate measurement data at multiple external measurement positions and uncertainty, and the dose rate contribution of the unit radioactivity of each source item division area to each measurement position, through an optimization problem, determining atarget function used for calculating a radioactivity value in each source item division area; and S3, calculating the target function and acquiring the radioactivity value in each source item divisionarea. By using the method and the system of the invention, based on a given source item division mode and a nuclide type, according to measured data and a dose rate contribution matrix, through a regularized least square principle, gamma radiation source item activity data which is relatively stable and matches with the accuracy of the measured data is inverted.
Owner:CHINA INST FOR RADIATION PROTECTION
Method and system for communicating multimedia using reconfigurable rateless codes and decoding in-process status feedback
ActiveUS9215457B2Avoid erroneous decodingImprove performanceOther decoding techniquesCode conversionComputer hardwareLeast squares optimization
Owner:MITSUBISHI ELECTRIC RES LAB INC
Cloud detecting method and system based on threshold value and regulation least square
ActiveCN104463212AHigh precisionImprove computing efficiencyCharacter and pattern recognitionCloud detectionCloud data
The invention provides a cloud detecting method and system based on a threshold value and a regulation least square. The method comprises the steps that a threshold value method is utilized for carrying out initial detecting on a remote sensing image and obtaining detected initial cloud data; a regulation least square method is utilized for classifying the initial cloud data and obtaining classified cloud data. According to the cloud detecting method and system based on the threshold value and the regulation least square, at first, the threshold value method is utilized for carrying out initial detecting on the remote sensing image and obtaining the detected initial cloud data, then the regulation least square method is utilized for classifying the initial cloud data and obtaining the classified cloud data, and therefore the precision of cloud detection is greatly improved; moreover, after initial detection is carried out, class numbers classified by using the regulation least square method are reduced, the complexity of calculation is reduced, and the calculation efficiency of cloud detection is improved.
Owner:SATELLITE ENVIRONMENT CENT MINIST OF ENVIRONMENTAL PROTECTION +1
Method and device for predicting drug-target interaction relationship
PendingCN112216353AAvoid matrix operationsAvoid long cycleChemical property predictionMolecular designDiseasePredictive methods
The invention discloses a method and device for predicting a drug-target interaction relationship. The method comprises the following steps: introducing drug-target interaction and target-disease interaction to construct a three-layer heterogeneous network of drug-target-disease; constructing a drug similarity matrix and a target similarity matrix based on the three-layer heterogeneous network, wherein the target similarity matrix comprises an inter-target Gaussian kernel similarity matrix and a target-disease Gaussian kernel similarity matrix; calculating a Kronecker product of the drug similarity matrix and the target similarity matrix, and obtaining a prediction result through a regularized least square method; and verifying the prediction result. Compared with a traditional predictionmethod, the method has the advantages that a more complete network structure model is adopted, a more complex similarity matrix space is established, brand-new drug-target interaction is predicted from more perspectives, ultra-scale matrix operation is avoided in the calculation process, and compared with a common FLapRLS method and a common RLS_Kron method, the method provided by the invention has better prediction performance.
Owner:CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
Discriminating speech and non-speech with regularized least squares
ActiveUS7853539B2Digital computer detailsCharacter and pattern recognitionSound discriminationNoise classification
Sound discrimination techniques are disclosed that can be employed, for example, in the task of discriminating speech and non-speech in a noisy environment and other noise classification applications. In one particular embodiment, a classifier system is provided that includes a linear Regularized Least Squares classifier used directly on a high-dimensional cortical representation of the target sound. The regularization constant lambda (λ) can be selected automatically, yielding a parameter-free learning system. In addition, the high-dimensional hyperplane can be viewed directly in the cortical space, leading to greater interpretability of the classifier results.
Owner:HONDA MOTOR CO LTD
Adaptive state estimation method for autoregressive moving average system and closed-loop control system
ActiveCN111258222AEliminate negative effectsAvoid negative effectsAdaptive controlMoving averageLoop control
The invention discloses a method and device for carrying out adaptive state estimation on an autoregressive moving average system with additive output noise and control variables and a closed-loop control system. The method comprises the following steps: performing state space implementation on the autoregressive moving average system of which a preset application background is provided with additive output noise and a control variable; modeling the additive output noise of the autoregressive moving average system by utilizing an L2 norm regular term; simultaneously estimating the state valueand the output noise of the autoregressive moving average system by using a regularization least square method; and taking a regularization parameter for adjusting the detection intensity of the output noise as an optimal regularization parameter when an error between the sample variance of an estimated residual error and the variance of actual system noise is minimized. According to the invention, the negative influence of the output noise caused by the autocorrelation can be eliminated under the condition that the output of the system has the autocorrelation, and the method for performing adaptive unbiased estimation on the state value of the system is provided.
Owner:重庆冲程科技有限公司
A system and method for image super-resolution reconstruction utilize standardized group sparsity regularization
ActiveCN109345453AQuality improvementPromote recoveryImage enhancementGeometric image transformationPattern recognitionVideo monitoring
The invention discloses an image super-resolution reconstruction system and a method which are regularized by sparse standardization group, and relates to the technical field of image restoration. Theinvention firstly adopts bilinear interpolation method to obtain initial estimation value of image super-resolution reconstruction; Then, by using the normalized sparse prior of image block group, the quality of super-resolution image reconstruction is improved by iterating the adaptive soft threshold filtering in PCA domain and the regularized least squares in pixel domain of image block group.The system comprises an input low-resolution image (00), an initialization module (10), a routing module (20), an image filtering module (30), an image reconstruction module (40) and an output high-resolution image (50). The image reconstruction module (40) also interacts with the routing module (20). The invention can improve the restoration effect of the high-frequency detail of the image and effectively improve the quality of the super-resolution image reconstruction. The invention is Suitable for video surveillance, medical imaging and other applications.
Owner:SOUTH CENTRAL UNIVERSITY FOR NATIONALITIES
Time-Frequency Domain Modal Parameter Identification Method Based on Nonlinear FM Component Decomposition
ActiveCN107329932BEasy to identifyEasy to useComplex mathematical operationsInterference resistanceFourier model
Owner:SHANGHAI JIAOTONG UNIV
Method and device for performing robust state estimation on state-limited nonlinear system
Owner:SOUTHWEST UNIVERSITY
Frequency domain fuzzy sub-peak suppression method for LTE external radiation source radar
ActiveCN111007475AReduce lossesImprove detection abilityWave based measurement systemsHigh level techniquesAlgorithmEngineering
The invention belongs to the technical field of radar signal processing, and particularly relates to frequency domain fuzzy subpeak suppression method for an LTE external radiation source radar. The method comprises the steps of obtaining an echo signal and a reference signal; suppressing the echo signal by using the reference signal through an extended cancellation algorithm to obtain a target reflection echo signal; demodulating the reference signal to obtain a standard reference signal; obtaining a regularized least square model according to the reference signal and the standard reference signal, and solving the regularized least square model to obtain a fuzzy correction factor of the reference signal; correcting the original reference signal according to the fuzzy correction factor toobtain a corrected reference signal; and performing coherent processing on the corrected reference signal and the target reflection echo signal to obtain a target detection result without a false peak. The method has the beneficial effects that the radar performance is improved, the fuzzy sub-peak suppression performance is high, and the adaptability and robustness are high.
Owner:XIDIAN UNIV
Image compression method based on active learning and semi-supervised learning
InactiveCN102622771BShorten the timeIncrease the compression ratioImage codingImage segmentationImage compression
Owner:ZHEJIANG UNIV
Cloud detection method and system based on threshold and regularized least squares
ActiveCN104463212BHigh precisionImprove computing efficiencyCharacter and pattern recognitionCloud detectionCloud data
The present invention provides a cloud detection method and system based on threshold and regularized least squares, including: using the threshold method to perform initial detection on remote sensing images, and obtaining initial cloud data after detection; Classify the above initial cloud data, and obtain the classified cloud data. The cloud detection method and system based on threshold and regularized least squares provided by the present invention first uses the threshold method to perform initial detection on remote sensing images and obtains the initial cloud data after detection, and then uses the regularized least squares method to detect the initial cloud data. Classify the data and obtain the classified cloud data, which greatly improves the accuracy of cloud detection, and, after the initial detection, reduces the number of classes classified using regularized least squares, thereby reducing the computational complexity and improving Computational efficiency of cloud detection.
Owner:SATELLITE ENVIRONMENT CENT MINIST OF ENVIRONMENTAL PROTECTION +1
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