Graph clustering method based on robust rank constraint sparse learning

Abstract

The invention discloses a graph clustering method based on robust rank constraint sparse learning. The method is used for solving the technical problem that an existing graph clustering method is poorin robustness. According to the technical scheme, a data similarity graph S is learned through a sparse representation method, and an initial target function is obtained in combination with an L2, 1norm; constructing an initial graph by adopting a k-nearest neighbor method, and constraining the obtained similar graph S in the neighborhood of the initial graph; and laplace rank constraint is added, so that the rank is equal to the number of the data points minus the number of the connected regions of the similar graph, and a final target function is obtained. Converting the constrained optimization problem of the target function into an unconstrained optimization problem by applying an augmented Lagrange multiplier method; alternately optimizing variables contained in the target function;updating parameters contained in the augmented Lagrange multiplier method at the end of each iteration; and after iteration reaches a termination condition, decomposing the similarity matrix according to the obtained solution to obtain a final clustering result. According to the method, the robustness of the method is improved by constructing the graph with high quality and utilizing the L2, 1 norm.

Description

technical field [0001] The invention relates to a graph clustering method, in particular to a graph clustering method based on robust rank constraint sparse learning. Background technique [0002] With the development of information technology, the information that people come into contact with and need to process every day shows a geometric growth. Faced with such a large-scale information resource, how to effectively organize and utilize them has become an urgent problem to be solved. In this context, a large amount of unlabeled data has greatly promoted the development of an important research direction in the field of data mining - clustering. Clustering first originated in taxonomy, which is the process of grouping physical or abstract objects into classes based on similarity. This process uses the characteristics of the sample as a basis for classification to ensure that individuals within the same class are as homogeneous as possible and as heterogeneous as possible ...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of poor robustness of existing graph clustering methods...

Images