Semi-supervised classification method capable of simultaneously learning affinity matrix and Laplacian regularized least square

Abstract

The invention discloses a semi-supervised classification method capable of simultaneously learning an affinity matrix and a Laplacian regularized least square, which mainly comprises the following steps: firstly, a joint model capable of simultaneously learning the affinity matrix and the Laplacian regularized least square is established according to a training sample; secondly, the block coordinate descent method is used to optimize all kinds of variables in the model; and finally, the soft label of the sample is obtained by a Laplacian regularized least square classifier, and the dimension with the largest element in a label vector is selected as the category of the sample. The invention effectively fuses the sparse self-representation problem of samples and the Laplacian regularized least square classifier, and realizes the simultaneous optimization and mutual improvement of the sample affinity matrix and the Laplacian regularized least square classifier in the learning process. Theinvention has an explicit classifier function, so that the problem of an external sample can be effectively handled. Compared with other semi-supervised classification methods, the method has more accurate classification accuracy and good application prospects.

Description

technical field [0001] The invention relates to the field of computer pattern recognition, in particular to a semi-supervised classification method for simultaneously learning an affinity matrix (AffinityMatrix) and a Laplacian Regularized Least Square (Lap-RLS). Background technique [0002] In practical applications, the performance of classification methods mainly depends on the number of labeled samples in the training samples. However, obtaining labeled samples in real life is very difficult, expensive and time-consuming, and requires a lot of effort by experts in the field. On the other hand, thanks to the rapid development of data sampling technology and computer hardware technology, it is very easy to obtain a large number of unlabeled samples, which makes semi-supervised learning (Semi-supervised learning) that uses a small number of labeled samples and a large number of unlabeled samples for training. -Supervised Learning (SSL) has become a research hotspot in the ...

AI Technical Summary

Problems solved by technology

However, this method belongs to the transductive learning method, that is, there is no explicit decision function to predict the label of unlabeled samples, so it cannot effectively deal with the problem of foreign samples.

In addition, the label propagation method used in its optimization process is not suitable for datasets with complex structures.

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