Multi-view spectral clustering algorithm based on tensor expansion

Abstract

The invention provides a multi-view spectral clustering algorithm based on tensor expansion. All view data is expressed on the basis of a tensor and is expanded by the n-Mode multiplication of the tensor, the multidimensional constraint relationship (high-dimensional structure information) of multi-view data is analyzed, and key structure information is stored in view of thoughts of low rank representation and sparse representation so as to establish a solving model based on the tensor expansion. In addition, a noise problem in a practical data acquisition process is considered, and the noise tensor is added to carry out noise-proof processing. Since a non-convex low-rank constraint condition is in the presence in an optimization problem, direct solving is difficult, an optimal object needs to be subjected to convex relaxation, and then, and an ADMM (Alternating Direction Method of Multipliers) algorithm is used for carrying out optimal solving. An experiment result of certain real datasets indicates that a multi-view spectral clustering effect can be effectively improved.

Description

technical field [0001] The present invention relates to the field of data mining, more specifically, to a multi-view spectrum clustering algorithm based on tensor expansion. Background technique [0002] The multi-view clustering problem mainly improves the performance of clustering by integrating useful information in multiple views. At present, related algorithm research can be roughly divided into three categories: based on multi-image fusion algorithm, based on collaborative training algorithm, and based on subspace learning algorithm. [0003] First, based on the multi-image fusion algorithm. The idea of ​​this type of method is to construct a graph structure for each view, and then fuse these graph structures. In 2007, the research of Professor Dengyong Zhou and Professor Christopher J.C.Burges of Microsoft Research showed that a random walk was defined for each graph structure, and then a Markov mixture model was defined for all random walks to integrate information...

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Problems solved by technology

[0006] However, for the multi-view clustering problem, the existing methods almost all use matrix to represent the data of each view, only considering the information fusion of the view itself, ignoring the spatial structure relationship information between views

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