Sample class classification method of atom Laplacian regularization-based semi-supervised dictionary learning

Abstract

The invention discloses a sample class classification method of atom Laplacian regularization-based semi-supervised dictionary learning. The method comprises: S1, constructing an atom Laplacian regularization-based semi-supervised dictionary learning model according to training samples; S2, using a block coordinate descent algorithm to optimize various variables in the semi-supervised dictionary learning model until convergence occurs; and S3, linearly reconstructing label vectors of test samples according to dictionary atom labels of solving and sparse codes of the unlabeled samples, and selecting dimensions of largest elements in the label vectors to use the same as classes to which the same belong. According to the method, dictionary atoms are considered as anchor data of reconstructinga training sample set to construct a similarity matrix among the dictionary atoms, thus graph structure information which is more robust for anomalous samples can be obtained, thus the unlabeled samples are forced to more effectively participate in a dictionary learning process, and a learned dictionary is enabled to have better sparse representation ability and classification discriminating ability.

Description

technical field [0001] The invention relates to the fields of computer pattern recognition and machine learning, in particular to a sample category classification method based on semi-supervised dictionary learning of atomic Laplacian graph regularization. Background technique [0002] Dictionary learning is the process of learning a set of over-complete bases (dictionary atoms) using ordinary dense training samples, so as to obtain the sparse representation of input samples under these dictionary atoms. It is widely used in image processing, such as image restoration, image compression and image classification, etc. In short, dictionary learning consists of two parts, the sparse representation and the learning dictionary stage. Therefore, this leads scholars to measure the performance of the learned dictionary from two perspectives: the sparse representation ability and the classification discrimination ability of the dictionary. The sparse representation ability of the d...

AI Technical Summary

Problems solved by technology

[0005] Supervised dictionary learning algorithms are all based on the condition that all training samples are labeled, and there are relatively few labeled data in real life and rely on the labeling of external "mentors". Therefore, when the number of labeled samples is small When , the classification effect of the learned dictionary will be affected

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