Multi-view clustering machine learning method with missing kernel

A machine learning, multi-view technology, applied in the field of computer vision and pattern recognition, can solve problems such as reducing clustering performance, and achieve good clustering effect.

Inactive Publication Date: 2019-01-01
聚时科技(上海)有限公司
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Problems solved by technology

Despite exhibiting good clustering performance in a variety of applications, the "two-stage" algorithms mentioned above share a common drawback. They separate the two processes of filling and clustering, which inhibits the The mutual coordination among them reduces the clustering performance

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  • Multi-view clustering machine learning method with missing kernel
  • Multi-view clustering machine learning method with missing kernel
  • Multi-view clustering machine learning method with missing kernel

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Embodiment Construction

[0047] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0048] 1. Kernel k-means clustering

[0049] Represents a collection of n samples, Represents a feature map that maps x to a regenerated kernel Hilbert space The objective function of kernel k-means clustering is to minimize the cluster assignment matrix Z ∈ {0,1} n×k The sum of squared errors can be expressed as the following optimization problem:

[0050]

[0051] in, and denote the size and center of the cth cluster, respectively.

[0052] The optimization problem described by equation (1) can be written in the following matrix-vector form:

[0053]

[0054] Amon...

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Abstract

The invention relates to a multi-view clustering machine learning method with missing kernel. The method fuses filling and clustering, fills missing kernel under the guidance of clustering, and clusters with the filled kernel. The method comprises the following steps: 1) obtaining a target data sample, mapping the target data sample to a multi-kernel space; 2) establishing a missing multi-core k-means clustering optimization objective function; 3) adopting a three-step alternating method to solve that missing multi-core k-means clustering optimization objective function to realize clustering.Compared with the prior art, the invention considers the joint optimization of filling and clustering, and has the advantages of good clustering effect and the like.

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 machine learning method with missing kernels. Background technique [0002] In recent years, a large amount of research has been devoted to designing efficient multi-view clustering algorithms. Their purpose is to cluster data through an optimal combination of base kernels. For example, the literature "Multiple kernel clustering" (B. Zhao, J.T. Kwok, and C. Zhang, in SDM, 2009, pp.638–649) proposes to find the maximum margin hyperplane, the best clustering label and the best kernel simultaneously. In the literature "Optimized data fusion for kernel k-means clustering" (S.Yu,L.-C.Tranchevent,X.Liu,W. J.A.K.Suykens, B.D.Moor, and Y.Moreau, IEEE TPAMI, vol.34, no.5, pp.1031–1039, 2012) proposed a new k-means kernel optimization algorithm to combine multiple data sources for data...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/23213
Inventor 郑军刘新旺
Owner 聚时科技(上海)有限公司
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