Multi-view clustering method based on non-negative matrix factorization and diversity-consistency

A non-negative matrix decomposition, multi-view technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of low clustering accuracy and normalized interactive information

Inactive Publication Date: 2018-11-09
XIDIAN UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to address the deficiencies in the prior art above, and propose a multi-view clustering method based on non-negative matrix decompo

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  • Multi-view clustering method based on non-negative matrix factorization and diversity-consistency
  • Multi-view clustering method based on non-negative matrix factorization and diversity-consistency
  • Multi-view clustering method based on non-negative matrix factorization and diversity-consistency

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Embodiment Construction

[0042] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0043] refer to figure 1 , a multi-view clustering method based on non-negative matrix factorization and diversity-consistency, including the following steps:

[0044] Step 1 Obtain the non-negative multi-view data of the original image set

[0045] Extract various image features of each image in the original image set to obtain non-negative multi-view data of the original image set Among them, m represents the mth view, and m=1,2,...,n v , n v Indicates the number of views.

[0046] Step 2 for non-negative multi-view data To normalize:

[0047] For non-negative multi-view data Each view data in is normalized to obtain normalized multi-view data

[0048] Step 3 Build multi-view data Corresponding basis matrix and coefficient matrix

[0049] multi-view data Spectral clustering is performed on each view data in t...

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Abstract

The present invention provides a multi-view clustering method based on non-negative matrix factorization and diversity-consistency. The technical problem is solved that the clustering precision and the normalization interaction information are low in a current multi-view clustering method. The method comprises the steps of: obtaining normalization non-negative multi-view data of an original imageset; constructing a base matrix, a coefficient matrix and a standard-similar indication matrix corresponding to the multi-view data; constructing a target function based on the non-negative matrix factorization and diversity-consistency multi-view clustering; obtaining an iteration updating expression of the base matrix, the coefficient matrix and the Laplacian matrix; obtaining the optimal valueof the standard-similar indication matrix; and performing K-mean clustering for the optimal value of the standard-similar indication matrix, and obtaining a clustering cluster corresponding to the multi-view data. The multi-view clustering method employs expression diversity and standard-similar consistency to learn the complementation and common information in the multi-view data so as to effectively improve the performances of the multi-view clustering, and can be applied to the field of biology information analysis and financial investment analysis, etc.

Description

technical field [0001] The invention belongs to the technical field of computer vision and pattern recognition, and relates to a multi-view clustering method, in particular to a multi-view clustering method based on non-negative matrix decomposition and diversity-consistency, which can be applied to biological information analysis and financial investment analytics and business applications. Background technique [0002] In recent years, the development of Internet information technology has made data an important part of today's society. With the development of multimedia technology, data has shown explosive growth, and massive volume has become a prominent feature of today's data. In addition, with the rapid development of information collection technology, people can obtain a large amount of data from different data sources. These heterogeneous data describe different characteristics of the same target from different angles, and the data of each data source is regarded a...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23213
Inventor 王秀美张天真高新波张越美郭丁宁李洁邓成田春娜
Owner XIDIAN UNIV
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