Back to Search
Start Over
Robust High-Order Manifold Constrained Low Rank Representation for Subspace Clustering.
- Source :
-
IEEE Transactions on Circuits & Systems for Video Technology . Feb2021, Vol. 31 Issue 2, p533-545. 13p. - Publication Year :
- 2021
-
Abstract
- Due to the effectiveness in learning the subspace structures, low-rank representation (LRR) and its variations have been widely applied in various fields, such as computer vision and pattern recognition. However, in real applications, it is a challenge to handle the complex noises. To address this problem, we propose a novel robust LRR method based on kernel risk-sensitive loss (KRSL) with high-order manifold constraint, called RHLRR, in which the KRSL is introduced to deal with the noises and the multiple hypergraph regularization term is used as a high order manifold constraint to effectively capture the locality, similarity and the intrinsic geometric information in data. Besides, an iterative algorithm based on the half-quadratic (HQ) and the accelerated block coordinate update (BCU) is developed. The experimental results demonstrate that the proposed method can outperform other state-of-the-art LRR variants. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PATTERN recognition systems
*HYPERGRAPHS
*GRAPH algorithms
*COMPUTER vision
Subjects
Details
- Language :
- English
- ISSN :
- 10518215
- Volume :
- 31
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Circuits & Systems for Video Technology
- Publication Type :
- Academic Journal
- Accession number :
- 148595714
- Full Text :
- https://doi.org/10.1109/TCSVT.2020.2989659