Name
Abstract | ||
|---|---|---|
We consider the problem of clustering a set of high-dimensional data points into sets of low-dimensional linear subspaces. The number of subspaces, their dimensions, and their orientations are unknown. We propose a simple and low-complexity clustering algorithm based on thresholding the correlations between the data points followed by spectral clustering. A probabilistic performance analysis shows that this algorithm succeeds even when the subspaces intersect, and when the dimensions of the subspaces scale (up to a log-factor) linearly in the ambient dimension. Moreover, we prove that the algorithm also succeeds for data points that are subject to erasures with the number of erasures scaling (up to a log-factor) linearly in the ambient dimension. Finally, we propose a simple scheme that provably detects outliers. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICASSP.2013.6638261 | Acoustics, Speech and Signal Processing |
Keywords | DocType | Volume |
data handling,pattern clustering,probability,high dimensional data points,low dimensional linear subspaces,low-complexity clustering algorithm,probabilistic performance analysis,spectral clustering,subspace clustering,thresholding clustering,erasures,outlier detection,principal angles,spectral clustering,subspace clustering | Conference | abs/1303.3716 |
ISSN | Citations | PageRank |
1520-6149 | 3 | 0.41 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Reinhard Heckel | 1 | 23 | 4.57 |
| Helmut Bölcskei | 2 | 969 | 65.85 |