Anchor graph structure-based semi-supervised data classification method of double Laplacian regularization

Abstract

The invention discloses an anchor graph structure-based semi-supervised data classification method of double Laplacian regularization. The method mainly comprises the following steps: firstly, carrying out clustering on a data set to obtain anchor point data which can approximatively indicate the entire data set, and calculating linear reconstruction weights between sample points and adjacent anchor points thereof through an FLAE method; then respectively constructing Laplacian regularization terms on the anchor points and Laplacian regularization terms on the sample points on the basis of a weight matrix between the sample points and the anchor points, and establishing an anchor graph structure-based semi-supervised classification model of double Laplacian regularization; and finally, using a zero-gradient method to parse and solve the model to obtain category soft-labels of the anchor point data, and using feature codes of unlabeled samples to linearly combine the category soft-labels of the anchor points, and discriminating categories of the unlabeled samples. Double-Laplacian-regularization constraints established by the method can better describe graph structure information among the samples, and thus realize higher classification and discriminating ability, and the method has very good application prospects.

Description

technical field [0001] The invention relates to the field of computer pattern recognition, in particular to a double Laplace regularization semi-supervised classification method based on an anchor graph structure. Background technique [0002] With the rapid development of science and technology, various fields are flooded with various data, and mining the important information hidden behind these data has attracted widespread attention of experts and scholars. As an important means of data information mining, data classification has a very wide range of applications in many fields such as image processing, text classification, object recognition and multimedia information retrieval. Generally speaking, the performance of the trained classifier is closely related to the number of labeled samples in the training process, the more the number of labeled samples, the better the performance of the trained classifier. However, most of the data obtained in real life is unlabeled, ...

AI Technical Summary

Problems solved by technology

However, the Laplacian diagram of anchor points focuses on describing the structural relationship between anchor points. When the number of anchor points is small, it cannot effectively describe the similarity information between samples. On the other hand, the Laplacian graph of samples Stu focuses on the local neighbor relationship of the sample. When the sample set contains noise and abnormal points, there will be a certain deviation

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