3 accepted papers
Completing a data matrix X has become an ubiquitous problem in modern data science, with motivations in recommender systems, computer vision, and networks inference, to name a few. One typical assumption is that X is low-rank. A more general model assumes that each column of X corresponds to one of…
This paper presents R2PCA, a random consensus method for robust principal component analysis. R2PCA takes RANSAC’s principle of using as little data as possible one step further. It iteratively selects small subsets of the data to identify pieces of the principal components, to then stitch them toge…
Subspace clustering with missing data (SCMD) is a useful tool for analyzing incomplete datasets. Let d be the ambient dimension, and r the dimension of the subspaces. Existing theory shows that Nk = O(r d) columns per subspace are necessary for SCMD, and Nk =O(min d^(log d), d^(r+1) ) are sufficient…