A Semi-Supervised Classification Method Based on Dimensional Weighting and Perspective Feature Consistency

Abstract

The invention provides a semi-supervised classification method based on dimension weighting and view feature consistency. First, adopt the adaptive local structure learning method to construct the similarity matrix of each view of the multi-view data; then, take the average of the similarity matrices of all views as the initial consistent similarity matrix, and construct a matrix based on dimension weighting and view A multi-view semi-supervised classification model with feature consistency; then, the model is solved using an alternate iterative update method until the final label matrix is ​​obtained; finally, the label of the sample is obtained according to the label matrix, and the sample classification is completed. The classification model constructed by the present invention combines the construction similarity matrix with label inference, which reduces the influence of composition quality on the classification results; and because the feature dimension in the perspective is weighted and the local structure information of the data is considered, better classification results can be obtained .

Description

technical field [0001] The invention belongs to the technical field of machine learning and data mining, and in particular relates to a semi-supervised classification method based on dimension weighting and visual angle feature consistency. Background technique [0002] With the advent of the era of big data, information in many real scenes can be obtained through different channels, different angles, different modes, and different features. Faced with these multi-source data, how to efficiently and accurately fuse these information through a certain strategy to complete specific tasks and apply them in actual scenarios has important research significance. [0003] Assuming that there is "complementarity" and "consistency" in multi-view data sets, multi-view learning refers to a method that describes the research object from multiple perspectives, and then integrates information from multiple perspectives for learning. Semi-supervised classification refers to using a small ...

AI Technical Summary

Problems solved by technology

However, these regression-based ideas above only consider the linear relationship between samples and labels, and are not satisfied with nonlinear relationships.

In graph-based semi-supervised classification, the quality of the constructed similarity graph will greatly affect the final classification results, and since the construction of the similarity graph and label inference are considered as two separate steps, the relationship between the two is ignored

In addition, these methods only consider the differences in features between views, and ignore the differences between dimensions within views, thereby ignoring the local structure information of the data.

Therefore, the classification accuracy of these methods suffers

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