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Multi-view image clustering method based on clustering adaptive canonical correlation analysis

A canonical correlation analysis, image clustering technology, applied in instruments, character and pattern recognition, computer parts and other directions, can solve problems such as clustering incompatibility

Active Publication Date: 2020-07-10
ANHUI UNIV OF SCI & TECH
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Problems solved by technology

[0004] To effectively overcome the clustering inadaptability of multi-view correlation learning in clustering tasks

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  • Multi-view image clustering method based on clustering adaptive canonical correlation analysis
  • Multi-view image clustering method based on clustering adaptive canonical correlation analysis
  • Multi-view image clustering method based on clustering adaptive canonical correlation analysis

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Embodiment Construction

[0066] In order to clarify the purpose, technical solutions and advantages of the present invention, the present invention will be further described in detail below in conjunction with specific implementation cases and the accompanying drawings.

[0067] The specific implementation process of the present invention includes the following steps:

[0068] (1) Convert each image from multiple perspectives into column vectors to form a sample matrix Where M is the number of perspectives, X (i) Is the sample matrix of the i-th (i=1, 2,...,M) view angle, d i Is X (i) The sample dimension of Representative X (i) The uth (u=1, 2,...,N) sample. Correspond to the same target x u (u=1,2,...,N) M samples.

[0069] (2) Initialize the class label indication matrix F.

[0070] Use MCCA to reduce the dimensionality of multi-view data, obtain multi-view low-dimensional fusion data, and then use k-means to obtain class labels of multi-view low-dimensional fusion data. The method of the present invent...

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Abstract

The invention discloses a multi-view image clustering method based on clustering self-adaptive canonical correlation analysis, and the method mainly constructs a self-adaptive optimization model of multi-view canonical correlation analysis and clustering and solves the problem of clustering inadaptability of multi-view correlation learning in a clustering task, thereby improving the clustering performance of multi-view images. The method comprises the following steps: (1) initializing a class label indication matrix of an original high-dimensional sample; (2) iteratively solving a related projection matrix, a class centroid matrix and a class label indication matrix; (3) directly obtaining a clustering result based on the solved class label indication matrix. Compared with the prior art, the multi-view image clustering method provided by the invention is more effective and robust.

Description

Technical field [0001] The invention relates to the technical fields of multi-view joint dimension reduction and image clustering, and is specifically a multi-view image clustering method based on clustering adaptive canonical correlation analysis. It can be applied to the fields of image retrieval, data mining and pattern recognition. Background technique [0002] In the field of pattern recognition and machine learning, how to effectively reduce the dimensionality of multi-view data is still a challenging research topic. Among all the methods of solving problems, Canonical Correlation Analysis (CCA) plays an important role. This method was first proposed by Hotelling to analyze the correlation between two variables. So far, scholars have proposed many variants of CCA to adapt to different practical applications. As a classic two-view dimensionality reduction method, it is difficult for CCA to process more than two views at the same time. In order to break through this limit...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/213G06F18/23213Y02T10/40
Inventor 苏树智王子莹朱彦敏高鹏连平昕瑞郜一玮
Owner ANHUI UNIV OF SCI & TECH
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