Name
Abstract | ||
|---|---|---|
In this paper, we study the k-support norm regularized matrix pursuit problem, which is regarded as the core formulation for several popular computer vision tasks. The k-support matrix norm, a convex relaxation of the matrix sparsity combined with the l(2)-norm penalty, generalizes the recently proposed k-support vector norm. The contributions of this work are two-fold. First, the proposed k-support matrix norm does not suffer from the disadvantages of existing matrix norms towards sparsity and/or low-rankness: 1) too sparse/dense, and/or 2) column independent. Second, we present an efficient procedure for k-support norm optimization, in which the computation of the key proximity operator is substantially accelerated by binary search. Extensive experiments on subspace segmentation, semi-supervised classification and sparse coding well demonstrate the superiority of the new regularizer over existing matrix-norm regularizers, and also the orders-of-magnitude speedup compared with the existing optimization procedure for the k-support norm. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10605-2_40 | COMPUTER VISION - ECCV 2014, PT II |
Keywords | Field | DocType |
k-support norm, subspace segmentation, semi-supervised classification, sparse coding | Computer science,Neural coding,Matrix (mathematics),Algorithm,Basis pursuit,Matrix norm,Operator (computer programming),Artificial intelligence,Norm (mathematics),Binary search algorithm,Machine learning,Speedup | Conference |
Volume | ISSN | Citations |
8690 | 0302-9743 | 11 |
PageRank | References | Authors |
0.54 | 22 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hanjiang Lai | 1 | 234 | 17.67 |
| Yan Pan | 2 | 179 | 19.23 |
| Canyi Lu | 3 | 673 | 15.91 |
| Yong Tang | 4 | 41 | 9.00 |
| Shuicheng Yan | 5 | 9701 | 359.54 |