Clustering method based on manifold learning and rank constraint

Abstract

In order to overcome the defects of low clustering precision and weak robustness in clustering segmentation by adopting a least square regression method, the invention provides a manifold learning andrank constraint-based clustering method, which comprises the following steps of: obtaining original data, preprocessing the original data, and constructing a feature matrix X of the original data; based on a k-nearest neighbor method, calculating similarity among elements in the feature matrix X by adopting a similarity measurement function to obtain a weight matrix W corresponding to the featurematrix X; taking the weight matrix W as an initial matrix of a low-rank representation matrix Z, solving the low-rank representation matrix Z through a least square regression method, and applying manifold constraint and rank constraint to the low-rank representation matrix Z to obtain a final target function; and converting the final objective function from a constrained problem to an unconstrained problem by adopting a Lagrange multiplier method, alternately iteratively optimizing variables in the final objective function until convergence to obtain an optimal low-rank representation matrixZ ', and then obtaining a clustering result by adopting a spectral clustering graph cutting method for the optimal low-rank representation matrix Z'.

Description

technical field [0001] The present invention relates to the technical field of pattern recognition, more specifically, to a clustering method based on manifold learning and rank constraints. Background technique [0002] The purpose of clustering is to segment data without any label information into its corresponding natural groups. There are many current clustering algorithms, such as hierarchical clustering, density clustering, K-means clustering, etc. Among them, the spectral clustering algorithm is a clustering method based on graph theory. The eigenvectors of the Adams matrix are analyzed to complete the clustering. Compared with the traditional clustering algorithm, it has the characteristics of being able to cluster on the sample space of any shape and converge to the global optimum, so it is widely used. The clustering-based spectral clustering method is usually divided into two steps: first, calculate an affinity matrix W for the input feature points to measure th...

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Problems solved by technology

[0004] The present invention provides a clustering method based on manifold learning and rank constraints in order to overcome the disadvantages of low clustering accuracy and weak robustness in clustering segmentation using the least squares regression method described in the prior art

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