Design method for linear discrimination of sparse representation classifier based on nuclear space

Abstract

The invention relates to a design method for linear discrimination of a sparse representation classifier based on nuclear space. The method comprises the following steps of reading training samples, performing nonlinear transformation on the training samples to transform the training samples to the high-dimensional nuclear space, learning the training samples of each kind in the high-dimensional nuclear space, finding out the contribution (namely the weight) made by each individual in the training samples of the kind to constructing the subspace of the training sample of the kind, forming dictionaries through products of the training samples of the kind and a weight matrix, and sequentially arranging the dictionaries of all kinds to form a large dictionary matrix; obtaining linear discrimination sparse codes of the test samples inside the nuclear space on the basis of the dictionary matrix, and performing fitting on the test samples through the dictionaries of each kind and linear discrimination coding corresponding to the dictionaries; adopting the kind with the minimum fitting error as the category of the test samples. It can be ensured that sparse codes of the samples of the same kind are concentrated, sparse codes of the samples of different kinds are dispersed, the sample discrimination is effectively improved, and the performance of the classifier is improved.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a design method of a kernel space-based linear discrimination sparse representation classifier. Background technique [0002] In addition to showing great charm in the field of computer vision, the dictionary learning method based on sparse representation has been successfully applied in face recognition, image classification, image noise reduction and other fields. The pattern recognition process usually includes two stages: feature extraction stage and classification stage. The quality of the classifier directly affects the recognition rate of the pattern recognition system, and the design of the classifier has always been one of the core issues in the pattern recognition research. [0003] At present, the main classifier design methods are as follows. [0004] 1. Support vector machine method (English: Support Vector Machine) [0005] The support vec...

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Problems solved by technology

[0015] The present invention aims at the above-mentioned shortcomings of large fitting error and low precision in the classifier designed by the existing classifier design method, and provides a design method of a linear discrimination sparse representation classifier based on kernel space, and the classifier designed by the method The output is both sparse and discriminative, which significantly improves the performance of pattern recognition

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