Paper Info
Title
Data-Dependent Sparsity For Subspace Clustering
Abstract
Subspace clustering is the process of assigning subspace memberships to a set of unlabeled data points assumed to have been drawn from the union of an unknown number of low-dimensional subspaces, possibly interlaced with outliers or other data corruptions. By exploiting the fact that each inlier point has a sparse representation with respect to a dictionary formed by all the other points, an l(1) regularized sparse subspace clustering (SSC) method has recently shown state-of-the-art robustness and practical extensibility in a variety of applications. But there remain important lingering weaknesses. In particular, the l(1) norm solution is highly sensitive, often in a detrimental direction, to the very types of data structures that motivate interest in subspace clustering to begin with, sometimes leading to poor segmentation accuracy. However, as an alternative source of sparsity, we argue that a certain data-dependent, non-convex penalty function can compensate for dictionary structure in a way that is especially germane to subspace clustering problems. For example, we demonstrate that this proposal displays a form of invariance to feature-space transformations and affine translations that commonly disrupt existing methods, and moreover, in important settings we reveal that its performance quality is lower bounded by the l(1) solution. Finally, we provide empirical comparisons on popular benchmarks that corroborate our theoretical findings and demonstrate superior performance when compared to recent state-of-the-art models.
Year
Venue
Field
2017
CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE (UAI2017)
Subspace clustering,Pattern recognition,Computer science,Data dependent,Artificial intelligence,Machine learning
DocType
Citations 
PageRank 
Conference
1
0.35
References 
Authors
13
4
Name
Order
Citations
PageRank
Bo Xin1415.13
Yizhou Wang2116286.04
Wen Gao311374741.77
David P. Wipf458446.31