A low-rank sparse representation image feature learning method based on Laplace regularization

Abstract

The invention discloses a low-rank sparse representation image feature learning method based on Laplace regularization, which comprises the following steps: (1) randomly dividing a data set into a training set and a test set; (2) constructing the undirected weight graph of training set and calculating its Laplace matrix; (3) initializing that feature extraction matrix to extract the initial feature of the training set; (4) designing a learning model of non-negative low-rank sparse representation; (5) using an LADMAP optimization method d to optimize the learning model, and obtaining the optimal feature extraction matrix and the optimal classifier model parameters; (6) carrying out predicative identification on the test set samples to verify the effect of feature extraction and classification accuracy. The method has the advantages of strong robustness, high recognition rate, wide adaptability and the like, and can be widely used for target recognition, image classification and the likeby carrying out feature extraction on image samples, retaining more information of the samples and having stronger discrimination.

Description

technical field [0001] The invention relates to a face image recognition method, in particular to a low-rank sparse representation image feature learning method based on Laplacian regularization. Background technique [0002] At present, in large-scale image feature learning, it is difficult to obtain labeled training samples, which makes it difficult to use some existing supervised feature learning techniques, and training samples with noise further limits their performance. [0003] Existing methods usually assume that image sample data are distributed in independent low-dimensional subspaces, or approximately span multiple low-dimensional subspaces, and have a low-rank sparse structure. Some methods utilize low-rank sparse constrained representation learning methods to learn the spatial structure of the original data while denoising the data. It finds the low-rank structure of the data through the nuclear norm, through the l 1 The norm finds the sparse structure of the ...

AI Technical Summary

Problems solved by technology

However, the existing feature learning methods usually do not associate feature learning with classification tasks, making the learned features less discriminative and less stable, resulting in damage to the overall performance of these methods.

Images