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Subspace clustering method and device for potential low-rank representation

A low-rank representation and clustering method technology, which is applied in complex mathematical operations, instruments, character and pattern recognition, etc., can solve problems such as insufficient performance, weak subspace clustering robustness, and insufficient low-rank representation samples. To achieve the effect of improving performance and enhancing robustness

Inactive Publication Date: 2020-06-19
GUANGDONG UNIV OF TECH
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  • Claims
  • Application Information

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Problems solved by technology

[0004] In order to solve the problems of insufficient low-rank representation samples, weak robustness and insufficient performance of potential low-rank representation subspace clustering in the existing subspace clustering, the present invention provides a potential low-rank representation subspace clustering Class Methods and Devices

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  • Subspace clustering method and device for potential low-rank representation
  • Subspace clustering method and device for potential low-rank representation

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Embodiment 1

[0068] This embodiment 1 provides a subspace clustering method for potential low-rank representations, such as figure 1 shown, including the following steps:

[0069] S1. Obtain data and preprocess it to obtain a feature matrix;

[0070] This preprocessing step can adopt well-known common methods in the art, such as preprocessing for image data, that is, to normalize and grayscale correct the target image, eliminate noise, and then extract edges, regions or textures from the target image features As experimental features, for example, Gabor features are extracted from face image data, and HOG features are extracted from handwritten data sets; the feature matrix X i =[x 1 , x 2 ,...,x N ]∈R D*N is a feature matrix composed of data vectors, each column vector in the feature matrix corresponds to a feature vector of a feature point, where D is the dimension of the feature space, and N is the number of feature points;

[0071] In order to facilitate subsequent data processin...

Embodiment 2

[0118] Embodiment 2 provides a corresponding implementation device for the subspace clustering method of potential low-rank representation provided in Embodiment 1, which further makes the method more practical. The subspace clustering device for potential low-rank representation provided in this embodiment is introduced below. The subspace clustering device for potential low-rank representation described below can correspond to the subspace clustering method for potential low-rank representation described above. refer to.

[0119] like figure 2 As shown, the device includes:

[0120] The data preprocessing module is used to obtain data and preprocess it to obtain a feature matrix, and perform normalization processing on each feature point in the feature matrix;

[0121] Optimize the objective function building block, construct the objective function of potential low-rank representation subspace clustering based on the feature matrix, and use the Schatten-p norm as the regu...

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Abstract

The invention discloses a subspace clustering method and device for potential low-rank representation, and the method comprises the steps: obtaining data, and carrying out the preprocessing of the data, and obtaining a feature matrix; potential low-rank representation subspace clustering of an unobserved data sample is considered, a Schatten-p norm is used as a regular term to replace a rank function, and a problem that an NP is difficult to solve is converted into a solvable problem; introducing an lp norm constraint error term to construct a potential low-rank representation subspace clustering optimization objective function; then solving the optimization objective function to obtain a low-rank representation matrix; calculating an affinity matrix based on the low-rank representation matrix; and calculating and segmenting the affinity matrix by using a spectral clustering algorithm to realize potential low-rank representation subspace clustering of the data. According to the method,the problems that low-rank representation samples are insufficient and rank functions are difficult to solve are solved, the robustness of potential low-rank representation subspace clustering is enhanced, and the performance of potential low-rank representation subspace clustering is improved.

Description

technical field [0001] The invention relates to the technical field of pattern recognition calculations, in particular to a subspace clustering method and device for latent low-rank representation. Background technique [0002] With the advancement of science and the development of artificial intelligence, pattern recognition processes and analyzes various forms of information representing things or phenomena, thereby describing, identifying, classifying and explaining things or phenomena. Subspace clustering widely appears in many application domains, such as images, videos, texts, etc. [0003] The importance of subspaces naturally leads to the difficult problem of subspace partitioning, which aims to partition (or group) the data into each cluster corresponding to the subspace. The main challenge of subspace segmentation is how to effectively deal with the coupling problem between noise correction and data segmentation. Latent low-rank representation subspace clustering...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06F17/15G06F17/16
CPCG06F17/15G06F17/16G06F18/2323G06F18/23213
Inventor 曹江中符益兰戴青云
Owner GUANGDONG UNIV OF TECH
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