Multi-view semi-supervised classification method for rapid seed random walk

Abstract

The invention relates to a multi-view semi-supervised classification method for rapid seed random walk, which comprises the following steps: firstly, calculating a similarity matrix and a transition probability matrix of each view of input multi-view data by adopting a Gaussian kernel function; secondly, establishing an initial distribution state of each view angle of the multi-view-angle data according to the category label of the multi-view-angle data for semi-supervised learning, and calculating an arrival probability matrix of a first transfer state of each view angle of the multi-view-angle data; and finally, iteratively calculating an arrival probability matrix of multiple transition states of each view angle of the multi-view-angle data, and performing weighted summation on the arrival probability matrixes of all the transition states of each view angle to obtain a reward matrix of each view angle so as to generate a category label of the multi-view-angle data for testing. According to the invention, various types of data such as images, texts and videos can be accurately and effectively classified only by using a small amount of supervision information, and the method has acertain practical value.

Description

technical field [0001] The invention relates to the fields of multi-view and semi-supervised learning, in particular to a multi-view semi-supervised classification method of fast seed random walk. Background technique [0002] Multi-view data are extremely common in practical applications, such as multi-camera image collection and multi-modal information acquisition. Data collected from heterogeneous sources usually contain a lot of redundant and irrelevant information, which may degrade the performance of learning algorithms. In multi-view data, each view captures partial information rather than complete information. The full representation is latent and redundant, making it difficult to extract useful information for the task to be learned. On the other hand, single-view learning or simple concatenation of all multi-view features is usually ineffective. Therefore, it is crucial to learn enough discriminative features and use multi-view-driven algorithms to mine effectiv...

AI Technical Summary

Problems solved by technology

However, multi-view semi-supervised classification is largely understudied, and these models still have some shortcomings.

High computational complexity is a major limitation that most algorithms need to address when faced with large-scale learning problems, so more work is required

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