Efficient L1-Norm Principal-Component Analysis via Bit Flipping

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Authors P. Markopoulos, S. Kundu, Shubham Chamadia, D. Pados
Journal/Conference Name IEEE Transactions on Signal Processing
Paper Category
Paper Abstract It was shown recently that the <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> L1-norm principal components (L1-PCs) of a real-valued data matrix <inline-formula><tex-math notation="LaTeX">$\mathbf X \in \mathbb {R}^{D \times N}$</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">$N$</tex-math> </inline-formula> data samples of <inline-formula><tex-math notation="LaTeX">$D$</tex-math></inline-formula> dimensions) can be exactly calculated with cost <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(2^{NK})$ </tex-math></inline-formula> or, when advantageous, <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(N^{dK - K + 1})$</tex-math></inline-formula> where <inline-formula><tex-math notation="LaTeX">$d=\mathrm{rank}(\mathbf X)$ </tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$K<d$</tex-math></inline-formula>. In applications where <inline-formula><tex-math notation="LaTeX">$\mathbf X$</tex-math></inline-formula> is large (e.g., “big” data of large <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> and/or “heavy” data of large <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula>), these costs are prohibitive. In this paper, we present a novel suboptimal algorithm for the calculation of the <inline-formula><tex-math notation="LaTeX">$K < d$</tex-math></inline-formula> L1-PCs of <inline-formula> <tex-math notation="LaTeX">$\mathbf X$</tex-math></inline-formula> of cost <inline-formula><tex-math notation="LaTeX"> $\mathcal O (ND \mathrm{min} \lbrace N,D\rbrace + N^2K^2(K^2 + d))$</tex-math></inline-formula>, 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 in data dimensionality reduction and disease diagnosis from genomic data.
Date of publication 2017
Code Programming Language Python
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