Name
Abstract | ||
|---|---|---|
Sparse Subspace Clustering (SSC) is one of the most popular clustering methods in computer vision. However, SSC solved by convex programming tools may suffer from noise and outliers. Orthogonal Matching Pursuit (OMP) can solve these problems effectively but may lose correct information representation and accuracy. To overcome these problems, a new Sparse Subspace Clustering method by Rotated Orthogonal Matching Pursuit (SSC-ROMP) is proposed in this paper, in which once some vector chooses another vector as its neighbor, it has to be rotated to avoid it being chosen when the selected vector chooses neighbors. Also, Nonnegative Matrix Factorization is used for dimension reduction in SSC-ROMP. The analysis and experiment results on synthetic data and several open datasets have demonstrated that our new approach can achieve better performances in terms of accuracy and information representation than other subspace clustering algorithms and keep a good sparsity of clustering representation. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | Sparse Subspace Clustering, Orthogonal Matching Pursuit, Vector Rotation, Nonnegative Matrix Factorization |
Field | DocType | ISSN |
Matching pursuit,Dimensionality reduction,Pattern recognition,Computer science,Synthetic data,Non-negative matrix factorization,Artificial intelligence,Cluster analysis,Convex optimization,Sparse matrix,Principal component analysis | Conference | 1522-4880 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Zhong | 1 | 10 | 9.00 |
| Zhu Yuesheng | 2 | 112 | 39.21 |
| Luo Guibo | 3 | 15 | 6.04 |