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A process centric view of robust PCA (RPCA) allows its fast approximateimplementation based on a special form o a deep neural network with weightsshared across all layers. However, empirically this fast approximation to RPCAfails to find representations that are parsemonious. We resolve these bad localminima by relaxing the elementwise L1 and L2 priors and instead utilize astructure inducing k-sparsity prior. In a discriminative classification taskthe newly learned representations outperform these from the originalapproximate RPCA formulation significantly.

Improving approximate RPCA with a k-sparsity prior