Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings |
| Subtitle of host publication | 2017 14th Conference on Computer and Robot Vision, CRV 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1-7 |
| Number of pages | 7 |
| ISBN (electronic) | 9781538628188 |
| ISBN (print) | 978-1-5386-2819-5 |
| Publication status | Published - 2 Jul 2017 |
| Event | 14th Conference on Computer and Robot Vision, CRV 2017 - Edmonton, Canada Duration: 17 May 2017 → 19 May 2017 |
Abstract
Sparse subspace clustering (SSC) is an elegant approach for unsupervised segmentation if the data points of each cluster are located in linear subspaces. This model applies, for instance, in motion segmentation if some restrictions on the camera model hold. SSC requires that problems based on the l1-norm are solved to infer which points belong to the same subspace. If these unknown subspaces are well-separated this algorithm is guaranteed to succeed. The question how the distribution of points on the same subspace effects their clustering has received less attention. One case has been reported in which points of the same model are erroneously classified to belong to different subspaces. In this work, it will be theoretically shown when and why such spurious clusters occur. This claim is further substantiated by experimental evidence. Two algorithms based on the Dantzig selector and subspace selector are proposed to overcome this problem, and good results are reported.
Keywords
- Clustering, Sparse, Subspace
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Signal Processing
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Proceedings: 2017 14th Conference on Computer and Robot Vision, CRV 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-7.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Unbiased sparse subspace clustering by selective pursuit
AU - Ackermann, Hanno
AU - Rosenhahn, Bodo
AU - Yang, Michael Ying
N1 - Funding information: The work is funded by DFG (German Research Foundation) AC, YA 351/2-1 and RO 4804/2-1. The authors gratefully acknowledge the support.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Sparse subspace clustering (SSC) is an elegant approach for unsupervised segmentation if the data points of each cluster are located in linear subspaces. This model applies, for instance, in motion segmentation if some restrictions on the camera model hold. SSC requires that problems based on the l1-norm are solved to infer which points belong to the same subspace. If these unknown subspaces are well-separated this algorithm is guaranteed to succeed. The question how the distribution of points on the same subspace effects their clustering has received less attention. One case has been reported in which points of the same model are erroneously classified to belong to different subspaces. In this work, it will be theoretically shown when and why such spurious clusters occur. This claim is further substantiated by experimental evidence. Two algorithms based on the Dantzig selector and subspace selector are proposed to overcome this problem, and good results are reported.
AB - Sparse subspace clustering (SSC) is an elegant approach for unsupervised segmentation if the data points of each cluster are located in linear subspaces. This model applies, for instance, in motion segmentation if some restrictions on the camera model hold. SSC requires that problems based on the l1-norm are solved to infer which points belong to the same subspace. If these unknown subspaces are well-separated this algorithm is guaranteed to succeed. The question how the distribution of points on the same subspace effects their clustering has received less attention. One case has been reported in which points of the same model are erroneously classified to belong to different subspaces. In this work, it will be theoretically shown when and why such spurious clusters occur. This claim is further substantiated by experimental evidence. Two algorithms based on the Dantzig selector and subspace selector are proposed to overcome this problem, and good results are reported.
KW - Clustering
KW - Sparse
KW - Subspace
UR - http://www.scopus.com/inward/record.url?scp=85048033558&partnerID=8YFLogxK
U2 - 10.48550/arXiv.1609.05057
DO - 10.48550/arXiv.1609.05057
M3 - Conference contribution
AN - SCOPUS:85048033558
SN - 978-1-5386-2819-5
SP - 1
EP - 7
BT - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th Conference on Computer and Robot Vision, CRV 2017
Y2 - 17 May 2017 through 19 May 2017
ER -