Multi-view clustering method based on multi-manifold dual graph regularized non-negative matrix factorization

Abstract

The invention provides a multi-view clustering method based on multi-manifold dual graph regularized non-negative matrix factorization. The multi-view clustering method comprises the following steps of: S10, acquiring views to be clustered; S20, constructing an adjacency matrix of a data graph and an adjacency matrix of a feature graph for each view to be clustered; S30, acquiring a target function of multi-manifold dual graph regularized non-negative matrix factorization through a consistency coefficient and multi-view local embedding; S40, conducting iterating a preset number of times by using an iterative weighting method according to the target function, and updating the adjacency matrix of the data graph of each view to be clustered, the adjacency matrix of the feature graph of each view to be clustered and graph regular terms to obtain a feature matrix of each view to be clustered; and S50, analyzing the feature matrix of each view to be clustered by using a k-means clustering algorithm to realize multi-view clustering. Compared with a traditional multi-view clustering method, the clustering method has the advantages that structural information and features contained in viewdata are more effectively utilized, clustering effect is greatly improved, and better clustering performance is brought.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a multi-view clustering method based on multi-manifold dual graph regularization non-negative matrix decomposition. Background technique [0002] In recent years, high-dimensional data has appeared in many fields, and its dimensionality reduction operation has attracted people's attention. Non-negative matrix factorization (NMF), as a commonly used dimensionality reduction method, aims to learn local-based feature representations, and has been widely used in various applied research. Clustering is a basic topic in machine learning and data mining, the purpose is to divide a set of data into several groups according to the similarity of data points. [0003] Generally speaking, the dataset will be composed of different views to provide compatible and complementary information, that is, extracting information from multiple views can achieve better clustering performance th...

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Problems solved by technology

[0005] In view of the above problems, the present invention provides a multi-view clustering method based on multi-manifold dual graph regularized non-negative matrix decomposition, which effectively solves the technical problem of poor clustering effect of existing multi-view clustering methods

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