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Proceedings Paper

Characterizing L1-norm best-fit subspaces
Author(s): J. Paul Brooks; José H. Dulá
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Paper Abstract

Fitting affine objects to data is the basis of many tools and methodologies in statistics, machine learning, and signal processing. The L1 norm is often employed to produce subspaces exhibiting a robustness to outliers and faulty observations. The L1-norm best-fit subspace problem is directly formulated as a nonlinear, nonconvex, and nondifferentiable optimization problem. The case when the subspace is a hyperplane can be solved to global optimality efficiently by solving a series of linear programs. The problem of finding the best-fit line has recently been shown to be NP-hard. We present necessary conditions for optimality for the best-fit subspace problem, and use them to characterize properties of optimal solutions.

Paper Details

Date Published: 5 May 2017
PDF: 8 pages
Proc. SPIE 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics, 1021103 (5 May 2017); doi: 10.1117/12.2263690
Show Author Affiliations
J. Paul Brooks, Virginia Commonwealth Univ. (United States)
José H. Dulá, Virginia Commonwealth Univ. (United States)


Published in SPIE Proceedings Vol. 10211:
Compressive Sensing VI: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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