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Rank-Constrained Block Diagonal Representation for Subspace Clustering

  • Yifang YangEmail author
  • Zhang Jie
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

The affinity matrix is a key in designing different subspace clustering methods. Many existing methods obtain correct clustering by indirectly pursuing block-diagonal affinity matrix. In this paper, we propose a novel subspace clustering method, called rank-constrained block diagonal representation (RCBDR), for subspace clustering. RCBDR method benefits mostly from three aspects: (1) the block diagonal affinity matrix is directly pursued by inducing rank constraint to Laplacian regularizer; (2) RCBDR guarantees not only between-cluster sparsity because of its block diagonal property, but also preserves the within-cluster correlation by considering the Frobenius norm of coefficient matrix; (3) a simple and efficient solver for RCBDR is proposed. Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of the proposed algorithm.

Keywords

Subspace clustering Spectral clustering Block diagonal representation 

Notes

Acknowledgement

This work was supported by the Scientific Research Plan Projects of Shaanxi Education Department (No.17JK0610); the Doctoral Scientific Research Foundation of Shaanxi Province (0108-134010006).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of ScienceXi’an Shiyou UniversityXi’anPeople’s Republic of China
  2. 2.School of Computer Science and EngineeringYulin Normal UniversityYulinPeople’s Republic of China

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