Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption
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The sparse representation classifier (SRC) proposed in Wright et al. (2009) has recently gained much attention from the machine learning community. It makes use of L1 minimization, and is known to work well for data satisfying a subspace assumption. In this paper, we use the notion of class dominance as well as a principal angle condition to investigate and validate the classification performance of SRC, without relying on L1 minimization and the subspace assumption. We prove that SRC can still work well using faster subset regression methods such as orthogonal matching pursuit and marginal regression, and its applicability is not limited to data satisfying the subspace assumption. We illustrate our theorems via various real data sets including face images, text features, and network data.
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