Design method of elastic network constraint self-interpretation sparse representation classifier

Abstract

The invention relates to a design method of an elastic network constraint self-interpretation sparse representation classifier. The method comprises the following steps: training samples are read, the training samples are linearly transformed to a high-dimensional kernel space, each type of the training samples are learnt in the high-dimensional space, a contribution (i.e., a weight) made by each individual in the type of the training sample to constructing a sub-space of the type of the training samples is found, and a dictionary is constructed by a product of the type of the training samples and a weight matrix; and elastic network coefficient coding of test samples in the kernel space is obtained through training the obtained sparse representation dictionaries, and finally, the test samples are fitted by use of each type of the dictionaries and the elastic network sparse coding corresponding to the dictionaries, fitting errors are calculated, and the type of minimum fitting errors are the type of the test samples. According to the invention, the method is integrated with the advantages of ridge regression and lasso regression, sparse coding features of the samples are enabled to sparse, the fitting errors are also quite small, classification errors are effectively reduced, and the identification performance of a 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 an elastic network constraint self-explanatory sparse representation classifier. Background technique [0002] Classifier design (Classifier Design) is an important research branch in the field of pattern recognition. Feature extraction is an important part of the pattern recognition system and a prerequisite for pattern classification and recognition. However, how to classify the extracted features to the maximum extent requires It is the ultimate goal of pattern recognition and the core unit of pattern recognition system. From the perspective of classification decision-making, effective classification and discrimination rules are the main factors to reduce the false recognition rate and improve the accuracy of pattern recognition. [0003] At present, the main classifier design methods are as follows. [0004] 1. Support vector machi...

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Problems solved by technology

[0013] Aiming at the above-mentioned shortcomings of large fitting error and low precision in the classifier designed by the existing classifier design method, the present invention provides a design method of a self-explanatory sparse representation classifier with elastic network constraints

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