A multi-view classifier and design method based on local features

Abstract

The invention discloses a multi-view classifier based on local features, comprising: an unlabeled multi-view large data set generation module, a global and local structural risk minimization classifier realization module, and a multi-view data local feature extraction module. Its advantage is that it effectively improves the classification performance of multi-view datasets through three aspects: effective data enhancement, construction of classifier design principles, and local feature extraction.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to a method for designing a multi-view classifier based on local features. Background technique [0002] At present, multi-view data is common in daily life. Taking entertainment webpages as an example, each webpage has text, audio, video, pictures, etc. Each type of information constitutes a view of the web page data, that is, a text view, an audio view, and the like. These perspectives can be used to identify an entertainment web page. And different categories of web pages will also have different representations of these perspectives. For example, political news webpages and entertainment webpages generally have different text content and video content. To classify these multi-view data, related classifiers, namely multi-view classifiers, are proposed. [0003] The current common multi-view classifiers are mainly designed from (1) collaborative training; (2) mult...

AI Technical Summary

Problems solved by technology

[0005] Problem 1. Effective data information needs to be enhanced: Through the development status of multi-core learning and multi-matrix learning, we found that the lack of effective data information for training will limit the performance of the classifier

However, this random generation method cannot guarantee that the generated samples must provide effective information, and it also ignores the role of the local characteristics of the sample.

[0006] Problem 2. The principle of classifier design is not perfect: Judging from the classifier models proposed based on multi-view learning, their design mostly follows global structural risk minimization (GSRM), local structural risk minimization (LSRM), and even experience Risk Minimization (ERM)

Because in any perspective, the relationship between the global features and local features of the data is different, and a simple difference comparison cannot reasonably reflect the relationship between the two features.

[0007] Problem 3. Limited local feature extraction: Local features are an important cornerstone for improving the effect of multi-view learning algorithms, especially for multi-core learning and multi-matrix learning. The lack of sufficient local features in the input samples is an important reason for the low performance of related classifiers. The reason, and no matter what kind of algorithm is multi-view learning, there are cases where new algorithms are proposed because they cannot reflect local features

The applicant and his team also proposed an improved kernel clustering algorithm to extract local features, but the extracted features have limited effect on improving the recognition rate of classifiers

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