Name
Abstract | ||
|---|---|---|
We consider the dimensionality reduction task under the scenario that data vectors lie on (or near by) multiple independent linear subspaces. We propose a robust dimensionality reduction algorithm, named as Low-Rank Embedding(LRE). In LRE, the affinity weights are calculated via low-rank representation and the embedding is yielded by spectral method. Owing to the affinity weight induced from low-rank model, LRE can reveal the subtle multiple subspace structure robustly. In the virtual of spectral method, LRE transforms the subtle multiple subspaces structure into multiple clusters in the low dimensional Euclidean space in which most of the ordinary algorithms can perform well. To demonstrate the advantage of the proposed LRE, we conducted comparative experiments on toy data sets and benchmark data sets. Experimental results confirmed that LRE is superior to other algorithms. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-36669-7_23 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
subtle multiple subspace structure,multiple cluster,data vector,subtle multiple subspaces structure,toy data set,proposed lre,benchmark data set,spectral method,affinity weight,dimensionality reduction,low-rank embedding,multiple independent linear subspaces | Discrete mathematics,Data set,Dimensionality reduction,Embedding,Subspace topology,Algorithm,Euclidean space,Linear subspace,Spectral method,Nonlinear dimensionality reduction,Mathematics | Conference |
Volume | Issue | ISSN |
7751 LNCS | null | 16113349 |
Citations | PageRank | References |
2 | 0.37 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chun-Guang Li | 1 | 310 | 17.35 |
| Xianbiao Qi | 2 | 103 | 8.25 |
| Jun Guo | 3 | 1579 | 137.24 |