2014 | OriginalPaper | Chapter
Efficient k-Support Matrix Pursuit
Authors : Hanjiang Lai, Yan Pan, Canyi Lu, Yong Tang, Shuicheng Yan
Published in: Computer Vision – ECCV 2014
Publisher: Springer International Publishing
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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.