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Multi-view feature fusion and clustering-oriented joint optimization method

A technology of feature fusion and joint optimization, applied in the field of multi-view learning

Pending Publication Date: 2020-10-23
FUZHOU UNIV
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  • Abstract
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  • Application Information

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Problems solved by technology

However, how to construct multiple views, evaluate these views and learn effective fusion methods is a great challenge

Method used

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  • Multi-view feature fusion and clustering-oriented joint optimization method
  • Multi-view feature fusion and clustering-oriented joint optimization method
  • Multi-view feature fusion and clustering-oriented joint optimization method

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Embodiment Construction

[0064] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0065] Please refer to figure 1 , the present invention provides a joint optimization algorithm for multi-view feature fusion and clustering, comprising the following steps:

[0066] Step S1: Get k views from the data And need to be clustered into c classes;

[0067] Step S2: Initialize parameter β i , γ, λ and Initialize matrix variables α, H, G, W, V and

[0068] Step S3: Describe the joint optimization problem oriented to multi-view feature fusion and clustering as a matrix decomposition paradigm of a shared indicator matrix and corresponding view coefficient matrices, write out a loss function, and confirm the optimization goal;

[0069] Step S4: Repeat the following process until convergence or reach the maximum number of iterations (t max ):

[0070] (1). Update α by Lagrange multiplier method t+1 .

[0071] (2). Update H with the clo...

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Abstract

The invention relates to a multi-view feature fusion and clustering-oriented joint optimization method. The method comprises the following steps: S1, obtaining a plurality of views of to-be-clusteredimage data and a clustered category number; s2, initializing related parameters and matrix variables; s3, expressing the multi-view feature fusion and clustering-oriented joint optimization problem asa shared indication matrix and a matrix decomposition normal form corresponding to each view coefficient matrix, writing a loss function, and confirming an optimization target; s4, updating various parameters until convergence or reaching the maximum number of iterations; and S5, calculating to obtain a converged shared clustering indication matrix, and allocating each sample label to obtain an optimal clustering result. The method is oriented to multi-view features, and accurate clustering can be realized.

Description

technical field [0001] The invention belongs to the field of multi-view learning, in particular to a joint optimization method for multi-view feature fusion and clustering. Background technique [0002] In recent years, multi-view technology has attracted more and more researchers' attention and has been widely used in many fields such as image recognition, image segmentation, natural language processing and multimedia understanding. Given a specific learning task, discovering hidden patterns and latent semantics from multiple views of these unlabeled data is called multi-view learning. Numerous studies have shown that multi-view learning is more effective, robust and general than single-view learning, because it considers the diversity of different views and fully exploits the common advantages of these views. However, how to construct multiple views, evaluate these views and learn effective fusion methods is a great challenge. Contents of the invention [0003] In view...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06F17/16
CPCG06F17/16G06F18/23G06F18/2431G06F18/253
Inventor 赵铁松黄爱萍裴舒凡陈炜玲王郑
Owner FUZHOU UNIV
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