Name
Abstract | ||
|---|---|---|
We propose three novel algorithms for simultaneous dimensionality reduction and clustering of data lying in a union of subspaces. Specifically, we describe methods that learn the projection of data and find the sparse and/or low-rank coefficients in the low-dimensional latent space. Cluster labels are then assigned by applying spectral clustering to a similarity matrix built from these representations. Efficient optimization methods are proposed and their non-linear extensions based on the kernel methods are presented. Various experiments show that the proposed methods perform better than many competitive subspace clustering methods. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/JSTSP.2015.2402643 | Selected Topics in Signal Processing, IEEE Journal of |
Keywords | Field | DocType |
subspace clustering,dimension reduction,kernel methods,low-rank subspace clustering,non-linear subspace clustering,sparse subspace clustering,data reduction,sparse matrices,spectral clustering,dimensionality reduction,kernel,similarity matrix,data clustering,clustering algorithms,cost function | Spectral clustering,Fuzzy clustering,Mathematical optimization,CURE data clustering algorithm,Clustering high-dimensional data,Data stream clustering,Correlation clustering,Pattern recognition,Computer science,Artificial intelligence,Biclustering,Cluster analysis | Journal |
Volume | Issue | ISSN |
PP | 99 | 1932-4553 |
Citations | PageRank | References |
25 | 0.72 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vishal M. Patel | 1 | 2251 | 110.69 |
| Hien Van Nguyen | 2 | 25 | 0.72 |
| rene victor valqui vidal | 3 | 5331 | 260.14 |