8f121ce07d74717e0b1f21d122e04521-Reviews.html

The paper presents a iterative algorithm to a robust principal component matrix factorization. The data is modeled as a sum of a low rank matrix approximation and a sparse noise matrix. Constraining the norms of the row and column factors of the former part of the sum allows to implement the nuclear norm minimization of the batch robust pca algorithm in an online stochastic gradient descent fashion. The approach taken by the authors resembles very much the approach taken by Marial et al 2009 (ICML) and 2010 (JMLR), only that the objective is slightly different (Robust PCA was not dealt with in the JMLR version). One difference between the JMLR and the ICML version is that a standard stochastic gradient version of the objective function did not perform as well as the proposed online dictionary learning approach (in which the statistics of the data are accumulated in the matrices A and B) in the ICML version, but in the JMLR version, a standard stochastic gradient implementation with appropriately chosen learning rate seemed to perform ok.