39 results about "Higher order tensor" patented technology

The invention discloses a tensor-model-based infrared dim target detecting method. The method comprises an off-line training stage and an online detecting stage, wherein the off-line training stage comprises S1, establishing a multi-characteristic sample database and S2, constructing high-order tensor models from the database, and the online detecting stage comprises S3, analyzing whether an unknown image sequence contains dim targets through the tensor models to achieve target detection. By establishing the tensor detection models, the tensor-model-based infrared dim target detecting method avoids the difficulty in identifying targets with complicated backgrounds and dim signal to noise ratios and can well detect targets in different scenarios and with dim signal to noise ratios and improve the detecting accuracy.

Computer-readable media having computer-executable instructions and apparatuses categorize documents or corpus of documents. A Tensor Space Model (TSM), which models the text by a higher-order tensor, represents a document or a corpus of documents. Supported by techniques of multilinear algebra, TSM provides a framework for analyzing the multifactor structures. TSM is further supported by operations and presented tools, such as the High-Order Singular Value Decomposition (HOSVD) for a reduction of the dimensions of the higher-order tensor. The dimensionally reduced tensor is compared with tensors that represent possible categories. Consequently, a category is selected for the document or corpus of documents. Experimental results on the dataset for 20 Newsgroups suggest that TSM is advantageous to a Vector Space Model (VSM) for text classification.

The invention discloses a millimeter wave system channel feedback method based on tensor parallel compression, and the method comprises the steps: 1, enabling a receiving end to obtain the current downlink channel state information, and then modeling the channel state information into a high-order tensor form; 2, enabling the receiving end to compress the channel state information tensor into a plurality of tensors with the same size by using a tensor parallel compression technology; 3, enabling the receiving end to decompos the compressed tensors through CANDECOMP / PARAFAC (CP) decomposition,and feeding back an obtained factor matrix to the transmitting end through an uplink feedback link; and 4, enabling the transmitting end to recover the factor matrix of the tensor of the original channel state information according to the factor matrix currently fed back to the transmitting end, and reconstructing the original channel state information through the recovered factor matrix. According to the invention, the high-dimensional characteristic of a millimeter wave MIMO-OFDM channel is utilized, and a high-order tensor model is combined with a tensor parallel compression technology, sparse characteristics of a downlink channel are not needed, and the feedback overhead can be effectively reduced under the condition that the feedback precision is ensured.

The invention discloses a similar block accumulation image denoising method and system based on Bayesian tensor decomposition. A plurality of image blocks are divided from an original image; similar image blocks are clustered to form a higher-order tensor; tensor decomposition is performed on the high-order tensor to obtain a joint probability of a tensor matrix and noise precision, a maximum log-likelihood model parameter and a hyper-parameter based on the joint probability are estimated approximately through an alternate iteration method and a denoised high-order tensor is obtained, and finally each image block in the denoised high-order tensor is restored to obtain a denoised low-order image.

Provided is a fiber direction distribution estimating method based on nonnegative higher-order tensor quasi-Newton searching. The method includes the followings steps: reading brain magnetic resonance data, obtaining a magnetic resonance signal S(g) in a gradient applying direction g and a magnetic resonance signal S0 in a gradient non-applying direction as well as gradient direction data, selecting an interested area that is needed, and calculating a diffusion attenuation signal S(g) / S0 in the area; modeling the diffusion attenuation signals S(g) / S0 of all voxels in the interested area one by one to form an ellipsoid distribution model with a diffusion form; and obtaining a diffusion function D(v) by calculating a coefficient vector c of a tensor, then calculating a diffusion function value of each sampling point, fitting the diffusion function values into a diffusion model, and searching an extremum and calculating the fiber direction. A higher-order Cartesian tensor is utilized to achieve fitting of a fiber direction distribution function square root, all fiber direction distribution functions are guaranteed to be nonnegative, the angle resolution is high, and a great experiment effect is achieved.

The invention relates to the technical field of medical instruments, provides a novel practical method for extracting MRI (magnetic resonance imaging) parameters of structural abnormality of white matters of a human brain, and aims to achieve the purpose of objectively evaluate the lesion degree by analyzing anisotropy of tissues through a certain characteristic extraction method. The invention adopts the technical scheme that the method for extracting high-order tensor characteristic parameters of diffusion kurtosis tensor imaging comprises the following steps: a subject collects diffusion weighted signals of tissues along a plurality of directions on a magnetic resonance scanner, then a second-order or fourth-order tensor that reflects diffusion and distribution probability density function of water molecules in the tissues can be obtained through fitting, and lesion characteristics of diffusion and distribution abnormality of the water molecules, caused by structural abnormality of the tissues, can be obtained through certain tensor analysis, wherein parameters from two different angles, including diffusion coefficient and kurtosis coefficient, are mainly included. The invention is mainly applied to design and manufacture of medical instruments.

The invention provides an MIMO radar two-dimensional direction of arrival estimation method based on constraint tensor decomposition. The method mainly solves the problems that an existing algorithm is high in calculation complexity and poor in estimation precision. According to the method, the high-order tensor model is introduced, the multiple linear relation of MIMO radar multi-pulse receiving data is effectively utilized, and the precision and resolution of direction-of-arrival estimation are improved. The method does not need iteration, is low in calculation complexity and stable in convergence, and can be used for multi-target detection tracking under the condition that the number of space targets is unknown. A high-order tensor model suitable for an MIMO radar with a plurality of transmitting sub-arrays is constructed, the tensor model is reconstructed, and singular value decomposition is performed on an obtained reconstructed matrix, so that a left singular matrix retains a Van der Monde structure constrained by an array structure; according to the special structure of the left singular matrix, phase information of a target coming direction is estimated from a sub-matrix of the left singular matrix by using an ESPRIT-like algorithm, and then two-dimensional direction-of-arrival estimation is realized.

The invention belongs to the technical field of social network community detection. The invention discloses a method and a device for detecting a large-scale social network community based on high-order tensor decomposition. The detection device based on the high-order tensor decomposition large-scale social network community comprises a network information acquisition module, a central control module, a community search module, a community sharing module, a network compression module, a community analysis module, an influence evaluation module and a display module. According to the method, ablock item tensor layer is constructed by adopting a block item tensor decomposition method through the network compression module to replace a full connection layer in an original social network, andthe characteristics of symmetry and exponential expression ability of the block item tensor layer are utilized, so that the parameter quantity of the full connection layer can be greatly compressed,and the classification precision of the original network can be maintained; meanwhile, the network topology structure can be better utilized through the community analysis module, and the analysis accuracy is high; and distinguishing the types of the core nodes is realized.

The invention discloses a three-dimensional heart magnetic resonance imaging method based on tensor composition sparse bound. According to the method, undersampling of whole heart three-dimensional K spatial data is achieved through a three-dimensional radial sampling track; sparse representation of a magnetic resonance image is achieved through high-order tensor decomposition, optimal sparse representation of whole heart three-dimensional magnetic resonance data is achieved, and magnetic resonance imaging accuracy is improved; L1 norms of tensor decomposition sparse data and composite regularization terms are combined to serve as bound terms, reconstruction of the undersampled three-dimensional heart magnetic resonance image is achieved through a rapid composite splitting algorithm. By the adoption of the method, magnetic resonance imaging scanning time is effectively shortened, the heart magnetic resonance imaging speed is increased, motion artifacts generated in the heart detection process can be conveniently eliminated, and three-dimensional heart magnetic resonance imaging accuracy is improved.

The present invention discloses a data cleaning method. The method comprises: acquiring to-be-cleaned data, and according to analysis for noise data distribution in the to-be-cleaned data, obtaining to-be-cleaned fields in the to-be-cleaned data; searching a field which can be subjected to dimension expansion in the to-be-cleaned data, and performing high-order tensor dimension-expanding on the field which can be subjected to dimension expansion to obtain M tensor fieldsets; and carrying out data cleaning on the to-be-cleaned fields by using tensor fields in the tensor fieldsets, which are associated with the to-be-cleaned fields. The present invention also discloses a data cleaning apparatus.

The invention discloses a channel state information positioning fingerprint construction method based on tensor decomposition. First, the collected original channel state information (Channel State Information, CSI) data is represented as a three-dimensional image; then the three-dimensional image is regarded as a third-order tensor; then the analysis model based on Parallel Factor (PARAFAC) The tensor decomposition algorithm combined with the Alternate Least Squares (ALS) iterative algorithm is used for tensor noise reduction processing; then, the tensor wavelet decomposition algorithm is used to perform a single-layer tensor on the three dimensions of the CSI image Quantitative wavelet decomposition, and the use of angular second-order moments to calculate the wavelet coefficients of each wavelet subcomponent; finally, the CSI positioning fingerprints corresponding to the coordinates of each reference point are obtained. The present invention makes full use of the characteristics that high-order tensors can describe data information and structures, expresses complex data in the form of tensors, finally realizes noise reduction and feature extraction of tensor images, and improves the ability of data processing and analysis.

The present invention discloses a millimeter wave system channel feedback method based on tensor parallel compression. The terminal uses tensor parallel compression technology to compress the channel state information tensor into several tensors of the same size; 3. The receiver uses CANDECOMP / PARAFAC (CP) decomposition to decompose the above compressed tensors respectively, and obtains The factor matrix is ​​fed back to the transmitting end through the uplink feedback link; 4. The transmitting end restores the factor matrix of the original channel state information tensor according to the factor matrix currently fed back to the transmitting end, and reproduces the factor matrix obtained by restoring To construct the original channel state information, the present invention utilizes the high-dimensional characteristics of the millimeter-wave MIMO-OFDM channel, uses a high-order tensor model combined with tensor parallel compression technology, does not require the sparse characteristics of the downlink channel, and can effectively ensure feedback accuracy. Reduce feedback overhead.

The invention relates to a tensor chain rank-based color image adaptive reconstruction method, and the method comprises the following steps: 1, processing a to-be-reconstructed image to generate a reference image and a to-be-complemented high-order tensor; 2, adding different offsets to the first-order tensor chain rank of the initialized tensor chain rank of the high-order tensor to be complemented, and generating m candidate tensor chain ranks; 3, performing minimization processing on each candidate tensor chain rank to obtain m reconstructed tensors, and converting the reconstructed tensor into a reconstructed image; 4, determining the structural similarity between the reconstructed image and the reference image, and selecting a candidate tensor chain rank corresponding to the reconstructed image with the highest structural similarity as a first-order tensor chain rank; 5, repeatedly executing the operations from the step 2 to the step 4 until the structural similarity between the reconstructed image and the reference image is greater than 0.95, and obtaining an optimal reconstructed image. According to the method, the missing image can be reconstructed without an original data structure.

A processing method for high-order tensor data is disclosed, which can avoid destroying the internal structure of the data during the vectorization process of the image observation sample set, is flexible and simple, can extract the discriminative features of the tensor samples, and simplifies the image observation samples Concentrating a large amount of redundant information in high-order tensor data improves the image processing speed. This processing method for high-order tensor data decomposes high-order tensor data into three parts: shared subspace components, individual subspace components, and noise parts; Quantitative data is expressed as a linear combination of a set of tensor bases and vector coefficients; the variational EM method is used to solve the base tensor and vector coefficients; a classifier is designed to classify the samples to be tested by comparing the marginal distribution of samples.

The invention discloses a multi-attribute volume data compression method based on high-order tensor approximation. It includes preprocessing multi-attribute volume data, decomposing high-order tensors of block data into factor matrices and core tensors, and obtaining approximate high-order tensors according to the reconstruction of factor matrices and core tensors. For each The attribute volume is fused and drawn to complete the multi-attribute volume data compression. The invention greatly reduces the compression rate of the tensor approximation, and at the same time retains the original data of the multi-attribute body, effectively reflecting the target characteristics of the data.