It is natural to assume a data matrix to be of low-rank in many machine learning and computer vision problems, e.g., matrix completion and subspace clustering. To ensure low-rank, the nuclear norm minimization is widely used as a rank approximation and minimization technique. The nuclear norm treats all the singular values through adding them together with equal weights. With this liner addition, large singulars will con-tribute too much and make the approximation very inaccurate. To reduce this undesirable effect, we make use of a first-order log-determinant approximation which assigns small weights to large singular values and is close to vanishing for small ones. A low-rank representation is obtained using this function and the affinity is then constructed based on angular in-formation. Experimentally we achieve promising results on face clustering and motion segmentation using this affinity.
|Number of pages||8|
|Journal||IPPTA: Quarterly Journal of Indian Pulp and Paper Technical Association|
|State||Published - Mar 1 2018|
Bibliographical noteFunding Information:
This work is supported by the National Natural Science Foundation of China under grant (61703363,11241005), Shanxi Scholarship Council of China (2015-093), Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province and Foundation Program of Yuncheng University under Grants (YQ-2012020, SWSX201603).
© 2018 Indian Pulp and Paper Technical Association. All rights reserved.
- Face Clustering
- Motion Segmentation
- Spectral Clustering
- Subspace Clustering
ASJC Scopus subject areas
- Chemistry (all)
- Chemical Engineering (all)
- Media Technology
- Materials Chemistry