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Unsupervised multi-view feature selection method based on graph learning and view weight learning

A feature selection method and feature selection technology, applied in character and pattern recognition, instruments, computer components, etc., can solve the problems of not being able to obtain the optimal similarity graph, assigning view weights without parameters, etc.

Inactive Publication Date: 2020-06-26
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

The invention solves the problem that the existing unsupervised multi-view feature selection algorithm cannot obtain the optimal similarity graph and the problem of non-parameter adaptive allocation of view weights

Method used

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  • Unsupervised multi-view feature selection method based on graph learning and view weight learning
  • Unsupervised multi-view feature selection method based on graph learning and view weight learning
  • Unsupervised multi-view feature selection method based on graph learning and view weight learning

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Embodiment Construction

[0056] The present invention will be further described below in conjunction with the accompanying drawings and embodiments, and the present invention includes but not limited to the following embodiments.

[0057] Such as figure 1 As shown, the present invention provides an unsupervised multi-view feature selection method based on graph learning and view weight learning, and its basic implementation process is as follows:

[0058] 1. Data preprocessing

[0059] After inputting the original multi-view data X, first of all for each view data X (v) Do preprocessing:

[0060] (1) Calculate the mean value of each row, and subtract each element from the mean value of the row to centralize the data;

[0061] (2) Calculate the 2-norm value of each row of data after centering, and then divide each element value at this time by the 2-norm value of the row where it is located to complete the data preprocessing process.

[0062] Use the preprocessed data as the data to be used in subs...

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Abstract

The invention provides an unsupervised multi-view feature selection method based on graph learning and view weight learning. The method comprises the following steps: firstly, constructing an unsupervised multi-view feature selection model based on graph learning and view weight learning, and solving the model to obtain a feature selection matrix of each view in multi-view data; secondly, sorting2-norm values of matrix row vectors according to features, and selecting to obtain corresponding features according to needs; according to the method, the optimal similarity matrix shared by all viewsis obtained through adaptive learning to describe the manifold structure of the original data, adaptive weight distribution is carried out, complementary information existing among different views can be mined, more comprehensive and more accurate support information is provided for a feature selection process, and thus more valuable features are selected.

Description

technical field [0001] The invention belongs to the technical field of machine learning, and in particular relates to an unsupervised multi-view feature selection method based on graph learning and view weight learning, which can realize effective dimensionality reduction of unlabeled multi-view data. Background technique [0002] Multi-view data can display data samples from different angles. For example, for a picture, its image features such as LBP, HOG, and SIFT can be extracted to represent the picture, and different image features constitute multi-view Different views of data samples. Therefore, multi-view data can provide more comprehensive and effective sample information than traditional single-view data. Multi-view data has shown great advantages in computer vision, machine learning, data mining and other fields, and multi-view learning has become a very important research direction in the above fields. However, the multi-view data obtained through different feat...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2113G06F18/2155
Inventor 王琦袁媛蒋旭
Owner NORTHWESTERN POLYTECHNICAL UNIV
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