WebCUR provides a stochastic approximate solution to a sparse regression problem: "pick the best k-column subset and do a regression on it" while sparse PCA methods involve soling 'almost convex' relaxations of nonconvex optimization problems. CUR approximations cannot be written as an SPCA type method, but the authors provide an SPCA method ... Webthe limited resources of the sparse GP may be allocated to closely model regions of parameter space that perform poorly and are therefore less important for optimization. We propose weighted-update online Gaussian processes (WOGP) as an alternative to typical sparse GP set selec-tion that is better suited to optimization; rather than tailor-

CUR from a Sparse Optimization Viewpoint Request PDF …

WebNov 1, 2010 · However, CUR takes a randomized algorithmic approach, whereas most sparse PCA methods are framed as convex optimization problems. In this paper, we try … WebNov 1, 2010 · However, CUR takes a randomized algorithmic approach, whereas most sparse PCA methods are framed as convex optimization problems. In this paper, we try … read the good earth

Optimal CUR matrix decompositions Proceedings of the forty …

WebAbstract. The CUR decomposition of an m × n matrix A finds an m × c matrix C with a subset of c < n columns of A, together with an r × n matrix R with a subset of r < m rows of A, as well as a c × r low-rank matrix U such that the matrix C U R approximates the matrix A, that is, ‖ A − C U R ‖ F 2 ≤ ( 1 + ε) ‖ A − A k ‖ F 2 ... WebIn 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 … WebSPCA approaches are related. It is the purpose of this paper to understand CUR decompositions from a sparse optimization viewpoint, thereby elucidating the … read the grand mudang saga