Deterministic CUR for Improved Large-Scale Data Analysis: An Empirical Study

Low-rank approximations which are computed from selected rows and columns of a given data matrix have attracted considerable attention lately. They have been proposed as an alternative to the SVD because they naturally lead to interpretable decompositions which was shown to be successful in application such as fraud detection, fMRI segmentation, and collaborative filtering. The CUR decomposition of large matrices, for example, samples rows and columns according to a probability distribution that depends on the Euclidean norm of rows or columns or on other measures of statistical leverage. At the same time, there are various deterministic approaches that do not resort to sampling and were found to often yield factorization of superior quality with respect to reconstruction accuracy. However, these are hardly applicable to large matrices as they typically suffer from high computational costs. Consequently, many practitioners in the field of data mining have abandon deterministic appr...

• Type: Journal
• Year: 2012
• Where: SDM
• Authors: Christian Thurau, Kristian Kersting, Christian Bauckhage
• Keywords: Complexity Class, Data Mining, Deterministic Approaches, SDM 2012, Time Complexity