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CUR from a Sparse Optimization Viewpoint
Jacob Bien · Ya Xu · Michael W Mahoney

Mon Dec 06 12:00 AM -- 12:00 AM (PST) @ None #None

The CUR decomposition provides an approximation of a matrix X that has low reconstruction error and that is sparse in the sense that the resulting approximation lies in the span of only a few columns of X. In this regard, it appears to be similar to many sparse PCA methods. However, CUR takes a randomized algorithmic approach whereas most sparse PCA methods are framed as convex optimization problems. In this paper, we try to understand CUR from a sparse optimization viewpoint. In particular, we show that CUR is implicitly optimizing a sparse regression objective and, furthermore, cannot be directly cast as a sparse PCA method. We observe that the sparsity attained by CUR possesses an interesting structure, which leads us to formulate a sparse PCA method that achieves a CUR-like sparsity.

Author Information

Jacob Bien (Stanford University)
Ya Xu (Stanford University)
Michael W Mahoney (UC Berkeley)

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