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Robust Principal Component Analysis with Side Information
International Conference on Machine Learning
The robust principal component analysis (robust PCA) problem has been considered in many machine learning applications, where the goal is to decompose the data matrix to a low rank part plus a sparse residual. While current approaches are developed by only considering the low rank plus sparse structure, in many applications, side information of row and/or column entities may also be given, and it is still unclear to what extent could such information help robust PCA. Thus, in this paper, wedblp:conf/icml/ChiangHD16 fatcat:7vponohn3jdend5ekgjwbu2z2q