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Multi-view subspace clustering method based on block diagonal representation and view consistency

A clustering method and multi-view technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problem that the multi-view clustering accuracy cannot meet the needs of practical applications.

Inactive Publication Date: 2019-09-20
GUANGDONG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

However, most of the existing multi-view clustering methods are spectral clustering based on ordinary undirected graphs, which can only describe the relationship between pairs of data points, resulting in the accuracy of multi-view clustering It cannot meet the needs of practical applications. In practical applications, it may be necessary to pay attention to the relationship between a certain data point and another part of the data points at the same time, which cannot be achieved by spectral clustering based on ordinary undirected graphs.

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  • Multi-view subspace clustering method based on block diagonal representation and view consistency
  • Multi-view subspace clustering method based on block diagonal representation and view consistency
  • Multi-view subspace clustering method based on block diagonal representation and view consistency

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Embodiment Construction

[0065] The accompanying drawings are for illustrative purposes only and cannot be construed as limiting the patent;

[0066] In order to better illustrate this embodiment, some parts in the drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0067] For those skilled in the art, it is understandable that some well-known structures and descriptions thereof may be omitted in the drawings.

[0068] The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0069] like figure 1 As shown, it is based on the block diagonal representation and view of this embodiment Figure 1 Flowchart of a consistent multi-view subspace clustering method.

[0070] The block-diagonal representation and visual Figure 1 The steps of the consistent multi-view subspace clustering method are as follows:

[0071] Step 1: Input the multi-view data set, and construct th...

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Abstract

The invention relates to the field of computer vision application, and provides a multi-view subspace clustering method based on block diagonal representation and view consistency, which can improve the multi-view data set clustering precision, and comprises the following steps of: constructing a multi-view data matrix according to an input multi-view data set; constructing a shared consistency representation matrix according to the multi-view data matrix to obtain an objective function; transforming the objective function and obtaining an augmented Lagrangian equation of the objective function; obtaining an updated expression of a variable of the Lagrangian equation through an augmented Lagrangian equation; initializing variables, setting iteration conditions or iteration times, performing iteration updating on the variables according to an updating expression, and outputting a shared consistency representation matrix which meets the iteration conditions or the iteration times as an optimal shared consistency representation matrix; and constructing a hypergraph through the optimal sharing consistency representation matrix, and then clustering the multi-view data set by using a spectral clustering method based on the hypergraph to obtain a clustering result of the multi-view data set.

Description

technical field [0001] The present invention relates to the field of computer vision applications, and more specifically, to a block-diagonal representation and visual Figure 1 Consistent multi-view subspace clustering method. Background technique [0002] Clustering is a powerful data analysis and data processing technique that uses the similarity between data to divide it into multiple clusters. From the perspective of machine learning, clustering is an unsupervised learning method that can cluster data with unknown category information to extract useful information. However, existing clustering methods are usually suitable for single-view data. If all views are concatenated into one view, and state-of-the-art clustering methods are used on this single view, there is still a problem of poor clustering performance, because each Each view has its own specific statistical properties, so this treatment is meaningless. In contrast, multi-view clustering can effectively handl...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2321G06F18/23
Inventor 刘威尹明
Owner GUANGDONG UNIV OF TECH
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