Efficient L1-Norm Principal-Component Analysis via Bit Flipping
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It was shown recently that the K L1-norm principal components (L1-PCs) of a real-valued data matrix X ∈ R^D × N (N data samples of D dimensions) can be exactly calculated with cost O(2^NK) or, when advantageous, O(N^dK - K + 1) where d=rank( X), K<d [1],[2]. In applications where X is large (e.g., "big" data of large N and/or "heavy" data of large d), these costs are prohibitive. In this work, we present a novel suboptimal algorithm for the calculation of the K < d L1-PCs of X of cost O(ND min{ N,D} + N^2(K^4 + dK^2) + dNK^3), which is comparable to that of standard (L2-norm) PC analysis. Our theoretical and experimental studies show that the proposed algorithm calculates the exact optimal L1-PCs with high frequency and achieves higher value in the L1-PC optimization metric than any known alternative algorithm of comparable computational cost. The superiority of the calculated L1-PCs over standard L2-PCs (singular vectors) in characterizing potentially faulty data/measurements is demonstrated with experiments on data dimensionality reduction and disease diagnosis from genomic data.
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