An Unsupervised Regularized Matrix Factorization Feature Selection Method

Abstract

The present invention provides an unsupervised regularized matrix factorization feature selection method (URMFFS method), which considers the correlation between selected features by introducing inner product regularization constraints, so that the selected feature subset can not only be well represents the original high-dimensional features and has low redundancy. The present invention also designs an iterative optimization algorithm to solve the URMFFS method. A large number of comparative experiments were carried out on the URMFFS method and the currently popular unsupervised feature selection method on six commonly used databases (AR10P, Yale, ORL, Jaffe, PIE10P and TOX-171). The experimental results show that the performance of the URMFFS method is remarkable. Outperforms other unsupervised feature selection methods.

Description

technical field [0001] The invention relates to the technical fields of signal processing and data analysis, in particular to an unsupervised regularization matrix decomposition feature selection method. Background technique [0002] With the increasing popularity of computer technology, social network informatization, and the Internet, a large amount of high-dimensional data, such as text, multimedia, video, and images, has emerged, and they come from different systems, sensors, and mobile devices. These high-dimensional data usually have the characteristics of diversity, complexity, and redundancy. If computers are used to directly process these high-dimensional data, it will require huge memory storage space and computing costs, and will cause "curse of dimensionality" in severe cases. problem, which reduces the computational efficiency and performance of the algorithm. The "curse of dimensionality" is to ensure a certain accuracy rate in calculations involving vectors, ...

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Problems solved by technology

[0013] The matrix factorization feature selection (Matrix Factorization Feature Selection, MFFS) algorithm successfully builds a bridge between matrix factorization and feature selection, and its performance is better than a large number of current feature selection methods, but the constraints of the MFFS method are too strict, and It is difficult to satisfy in practical applications, and also ignores the correlation between features, resulting in a certain redundancy in the selected feature subset rather than the optimal feature subset

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