We address the scalability issues in low-rank matrix learning problems. Usually, these problems resort to solving nuclear norm regularized optimization problems (NNROPs), which often suffer from high computational complexities if based on existing solvers, especially under large-scale settings. Based on the fact that the optimal solution matrix to an NNROP is often low-rank, we revisit the classic mechanism of low-rank matrix factorization, based on which we present an active subspace algorithm for efﬁciently solving NNROPs by transforming large-scale NNROPs into small-scale problems. The transformation is achieved by factorizing the large-size solution matrix into the product of a small-size orthonormal matrix (active subspace) and another small-size matrix. Although such a transformation generally leads to non-convex problems, we show that suboptimal solution can be found by the augmented Lagrange alternating direction method. For the robust PCA (RPCA) (Candes et al., 2009) problem, which is a typical example of NNROPs, theoretical results verify sub-optimality of the solution produced by our algorithm. For the general NNROPs, we empirically show that our algorithm signiﬁcantly reduces the computational complexity without loss of optimality

The implementation is on Guangcan Liu's page entitled 'solving the rank constrained RPCA problem (released on Nov 2012). It will be added shortly to the Advanced Matrix Factorization Jungle page.

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