An unsupervised feature selection method based on mutual information and fractal dimension

Abstract

The invention discloses an unsupervised feature selection method which combines mutual information and fractal dimension to solve the problem that the information fusion performance of subspace learning algorithm is degraded due to redundancy and uncorrelated features contained in multi-dimensional original features. Firstly, an original feature extraction method is used to extract the multi-dimensional feature information of the product, and the multi-dimensional feature information of the product in time domain, frequency domain and time-frequency domain is obtained. Secondly, based on the definition of mutual information and considering the redundancy and correlation among multi-dimensional features, the feature importance is sorted to obtain the sorted multi-dimensional feature set. Then, based on fractal theory, the feature subsets of sorted multidimensional feature sets are selected by fractal dimension feature subset evaluation criteria, and the optimal feature subsets are obtained. Finally, the subspace learning method is used to reduce the dimension of the optimal feature subset, and the product comprehensive features are obtained. On the basis of comprehensively considering feature redundancy and correlation, the method removes features with small correlation degree with extra multi-dimensional original feature set and large redundancy degree, improves information fusion performance of subspace learning method, and simultaneously obtains product comprehensive features.

Description

technical field [0001] Aiming at the problem of reduced information fusion performance of subspace learning algorithm caused by redundant and irrelevant features contained in multidimensional features, the present invention proposes an unsupervised feature selection method based on mutual information and fractal dimension, which belongs to the technical field of feature extraction. Background technique [0002] With the development of scientific and technological research, in the fields of mechanical engineering, data mining, image processing, information retrieval, genome engineering and other fields, the research objects are becoming more and more complex, and the amount of data obtained from them is increasing rapidly, such as product failure data, gene Data, text information, high-resolution image information, etc., the feature dimension is gradually increasing. Multi-dimensional features are usually sparse, and information between arbitrary features may overlap. At the ...

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of reduced performance of subspace learning algorithm information fusion caused by redundant and irrelevant features contained in the above-mentioned multi-dimensional features, and propose an unsupervised feature selection method based on mutual information and fractal dimension

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