Name
Abstract | ||
|---|---|---|
Traditional subspace clustering models generally adopt the hypothesis of additive noise, which, however, dose not always hold. When it comes to multiplicative noise corruption, these models usually have poor performance. Therefore, we propose a novel model for robust subspace clustering with multiplicative noise corruption to alleviate this problem, which is the key contribution of this work. The proposed model is evaluated on the Extend Yale B and MNIST datasets and the experimental results show that our method achieves favorable performance against the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-67777-4_41 | INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING, ISCIDE 2017 |
Keywords | Field | DocType |
Subspace clustering, Multiplicative noise | Subspace clustering,MNIST database,Pattern recognition,Computer science,Artificial intelligence,Multiplicative noise,Corruption | Conference |
Volume | ISSN | Citations |
10559 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 23 | 2 |