Publication Type
Journal Article
Version
acceptedVersion
Publication Date
8-2019
Abstract
The current two-step clustering methods separately learn the similarity matrix and conduct k means clustering. Moreover, the similarity matrix is learnt from the original data, which usually contain noise. As a consequence, these clustering methods cannot achieve good clustering results. To address these issues, this paper proposes a new graph clustering methods (namely Low-rank Sparse Subspace clustering (LSS)) to simultaneously learn the similarity matrix and conduct the clustering from the low-dimensional feature space of the original data. Specifically, the proposed LSS integrates the learning of similarity matrix of the original feature space, the learning of similarity matrix of the low-dimensional space, the transformation matrix for finding the low-dimensional feature space of the original data, and the low rank constraint making the similarity matrix of low-dimensional space to be the final clustering results, in a framework. Moreover, we propose an iterative optimization method to adaptively adjust each of the processes towards the goal of clustering performance, and thus enabling to output good clustering results. Extensive experiments were conducted on the synthetic datasets and benchmark datasets, and experimental results showed that our proposed LSS method achieved the best clustering performance in terms of two evaluation metrics (i.e., clustering ACCuracy (ACC) and Normalized Mutual Information (NMI)), compared to the state-of-the-art clustering methods.
Keywords
Affinity matrix, Clustering methods, Correlation, Feature extraction, Feature selection, Laplace equations, Redundancy, Sparse matrices, spectral clustering, subspace learning, Technological innovation
Discipline
Computer Engineering | Databases and Information Systems
Publication
IEEE Transactions on Knowledge and Data Engineering
Volume
31
Issue
8
First Page
1532
Last Page
1543
ISSN
1041-4347
Identifier
10.1109/TKDE.2018.2858782
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
ZHU, Xiaofeng; ZHANG, Shichao; LI, Yonggang; ZHANG, Jilian; YANG, Lifeng; and FANG, Yue.
Low-rank sparse subspace for spectral clustering. (2019). IEEE Transactions on Knowledge and Data Engineering. 31, (8), 1532-1543.
Available at: https://ink.library.smu.edu.sg/sis_research/4093
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TKDE.2018.2858782