Pattern recognition classification method expressed based on grouping sparsity

Abstract

The invention discloses a pattern recognition classification method expressed based on grouping sparsity, comprising the steps of: obtaining an initial expression of a sample to be recognized by solving a least square solution of a linear equation; compensating a smaller grouping coefficient in the solution space of the linear equation, gradually enhancing the sparsity of solution vectors in the meaning of a grouping sparse model, and carrying out repeated iteration until constringency to obtain the grouping sparse expression of the sample; and judging the classification of the sample to be recognized as the largest grouping of the corresponding coefficient according to the obtained sparsity, and balancing the confidence coefficient by the concentration degree of the distribution in each group of the coefficient with the sparsity. The grouping model adopted by the invention is more suitable for the requirement on the classification, and improves the recognition capability. The sparsity of the solution is improved by combining the method of compensating the coefficient in the solution space, and the calculation amount is reduced. The method is not only suitable for the classification of pattern recognition, but also can be used in the fields of compressed sensing, and the like, and has wide application prospect.

Description

technical field [0001] The invention relates to the technical field of pattern recognition and sparse representation theory, in particular to a classification method based on a grouping sparse model. Background technique [0002] Sparsity plays an important role in pattern recognition methods. Support Vector Machine (SupportVector Machine, SVM) achieves a stronger generalization ability than traditional neural network methods by selecting support vectors that only account for a small proportion of the entire training sample set and constructing the best classification boundary accordingly, and overcomes over-learning. The problem. Relevance Vector Machine (RVM) introduces a probability model into SVM to make the coefficients corresponding to the support vectors sparser and achieve better performance than the support vector machine. In recent years, with the rapid development of sparse representation theory, a new pattern recognition method has emerged, which directly uses ...

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Problems solved by technology

[0008] The above optimization problem is an NP-hard combinatorial programming problem, and there is no algorithm that can find the solution of this problem in polynomial time

The disadvantage of this method is that it relies on the distribution of the representation coefficients of the samples on the training set samples in each category to identify the samples. However, the model on which these sparse representation coefficients are obtained tends to reduce the number of sample individuals required for representation. number instead of number of categories

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