Name
Abstract | ||
|---|---|---|
Outlier detection and cluster number estimation is an important issue for clustering real data. This paper focuses on spectral clustering, a time-tested clustering method, and reveals its important properties related to outliers. The highlights of this paper are the following two mathematical observations: first, spectral clustering's intrinsic property of an outlier cluster formation, and second, the singularity of an outlier cluster with a valid cluster number. Based on these observations, we designed a function that evaluates clustering and outlier detection results. In experiments, we prepared two scenarios, face clustering in photo album and person re-identification in a camera network. We confirmed that the proposed method detects outliers and estimates the number of clusters properly in both problems. Our method outperforms state-of-the-art methods in both the 128-dimensional sparse space for face clustering and the 4,096-dimensional non-sparse space for person re-identification. |
Year | Venue | DocType |
|---|---|---|
2017 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1703.01028 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takuro Ina | 1 | 0 | 0.34 |
| Atsushi Hashimoto | 2 | 40 | 13.33 |
| Masaaki Iiyama | 3 | 17 | 14.23 |
| Hidekazu Kasahara | 4 | 4 | 2.45 |
| Mikihiko Mori | 5 | 16 | 6.54 |
| Michihiko Minoh | 6 | 349 | 58.69 |