Subspace clustering method based on high-dimensional overlapping data analysis

Abstract

The invention provides a subspace clustering method based on high-dimensional overlapping data analysis and relates to the technical field of machine learning. The method builds a weighted mixed normsubspace representation model for a data matrix that needs clustering; obtaining an optimized coefficient matrix in the weighted mixed norm subspace representation model by using a linear alternatingdirection method; establishing a similarity matrix based on the optimized coefficient matrix; dividing the similarity matrix into subspaces by using a spectral clustering algorithm to obtain an initial clustering results; establishing an overlapping probability model of the subspaces; applying the overlapping probability model to an initial subspace division result to determine the overlapping ofthe subspaces; verifying a subspace clustering result to obtain a final clustering result. The subspace clustering method based on high-dimensional overlapping data analysis can improve the density ofthe same subspace data and the sparsity of different subspace data, and improve the accuracy of clustering.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a subspace clustering method based on high-dimensional overlapping data analysis. Background technique [0002] Cluster analysis method is one of the basic methods for people to recognize and understand the world, and it is an important method for obtaining information. It is one of the important research contents in the field of machine learning, and it is also widely used in medical biological analysis, statistics and computer vision. Clustering groups data only based on the information found in the data describing objects and their relationships. Its goal is to ensure that objects within a class are similar to each other, while objects in different groups are different, and objects within a class are similar. The greater the difference, the greater the difference between classes, indicating that the clustering effect is more ideal. Traditional clustering methods can...

AI Technical Summary

Problems solved by technology

Although the existing subspace clustering methods can solve the difficult problem of high-dimensional data clustering, the following problems still exist in the actual processing process: (1) The sparse problem of different subspaces

(3) Noise interference in high-dimensional data

The existing subspace clustering algorithms are all hard partition methods, each data is only allowed to be divided into one cluster, and cannot deal with the overlap problem between data, and cannot be verified in time when a clustering error occurs

Images