Open AccessSubspace Clustering by Integrating Sparseness and Spatial-Closeness Priors
Author(s) -
Zhe Li,
Haodong Pei,
Liang He,
Jiaming Liu,
Jiaxin Hu,
Dongji Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1631/1/012145
Subject(s) - cluster analysis , prior probability , subspace topology , computer science , mathematics , dimensionality reduction , affinity propagation , kernel (algebra) , pattern recognition (psychology) , closeness , correlation clustering , representation (politics) , artificial intelligence , canopy clustering algorithm , combinatorics , bayesian probability , mathematical analysis , politics , political science , law
How to construct an effective sample affinity matrix is an important problem for subspace clustering, and most existing subspace clustering algorithms pursue the affinity matrix in a single space. In this paper, we propose a novel computational framework for subspace clustering, called Complementary Subspace Clustering (CSC) at first, where the affinity matrix is constructed in a pair of complementary spaces which provide different and complementary constraints on the affinity matrix. Many existing structural priors on self representation and dimensionality reduction can be seamlessly integrated into the CSC framework. Then under this framework, we explore a simple and effective subspace clustering algorithm by respectively introducing two basic priors - sparse representation and spatial closeness - into the referred pair of spaces. Moreover, a kernel variant of the proposed clustering algorithm is present. Extensive experimental results demonstrate that although only basic priors are involved, the explored algorithms from the CSC framework can improve the clustering performances significantly when the number of the sample classes is relatively big.