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Multi-view clustering method based on Laplacian regularization and rank constraint

A clustering method, multi-view technology, applied in the field of computer vision and pattern recognition, to achieve the effect of improving accuracy

Active Publication Date: 2019-07-09
SOUTH CHINA UNIV OF TECH
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

However, few methods consider guaranteeing the local characteristics of the original views, and directly limit the rank of the common subspace to obtain the exact number of clusters

Method used

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  • Multi-view clustering method based on Laplacian regularization and rank constraint
  • Multi-view clustering method based on Laplacian regularization and rank constraint
  • Multi-view clustering method based on Laplacian regularization and rank constraint

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Embodiment Construction

[0046] The present invention will be further described below in conjunction with specific examples.

[0047] Such as figure 1 and figure 2 As shown, the multi-view clustering method based on Laplacian regularization and rank constraints provided in this embodiment uses the sports news data on the BBC website to evaluate the method of the present invention, including the following steps:

[0048] 1) Obtain multi-view data, including a total of 737 news reports from 5 categories on the BBC sports news website, and each report contains data from three views.

[0049] 2) Preprocess the characteristic data from different sources respectively, including:

[0050] 2.1) Data cleaning: for the missing part in the acquired data, use the cubic spline interpolation method for interpolation, and for the extremely large or extremely small abnormal values ​​in the data, replace them by taking the average value;

[0051] 2.2) Normalize the cleaned data: linearly transform the cleaned data...

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Abstract

The invention discloses a multi-view clustering method based on Laplace regularization and rank constraint. The multi-view clustering method comprises the following steps: 1) obtaining multi-view data; 2) preprocessing the multi-view data; 3) selecting a required similarity measure, and calculating a similarity matrix; 4) fusing prior information based on a similarity network between features, andprojecting the data of each source view to a public low-dimensional subspace constrained by a rank;. According to the method, the local characteristics of all view data are considered, so that the data approaching in a single view are still approaching in a public subspace, the rank of the public subspace is constrained, an exact number of clustering clusters can be obtained, and the clustering accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of computer vision and pattern recognition, in particular to a multi-view clustering method based on Laplacian regularization and rank constraints, which can be used for image and text data mining and the like. Background technique [0002] With the rapid development of computer technology and the explosive growth of data, it is very meaningful to extract useful information from massive data. Cluster analysis refers to the analytical process of grouping a collection of physical or abstract objects into multiple classes consisting of similar objects. It is an important human behavior. The goal of cluster analysis is to collect data to classify on the basis of similarity. Clustering has roots in many fields, including mathematics, computer science, statistics, biology, and economics. In different application fields, many clustering techniques have been developed. These technical methods are used to describe...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23G06F18/22
Inventor 蔡宏民周伟伟
Owner SOUTH CHINA UNIV OF TECH
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