Abstract
In this paper, we consider the problem of incrementally computing low-rank approximations of matrices. We propose an incremental low rank approximation algorithm of a sequence of matrices instead of one single matrix. Based on the generalized Low Rank Approximation of matrices approach, the proposed method uses an iterative approach to update the generated low rank structures. We tested the proposed incremental learning method on five face databases to evaluate its efficiency in terms of recognition rate and compared its performance with the Incremental Singular Value Decomposition method. We also showed that the overall reconstruction error is kept bounded during the incremental learning process.
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