Paper Info
Title
GLIMPS: A Greedy Mixed-Integer Approach for Super Robust Matched Subspace Detection
Abstract
Due to diverse nature of data acquisition and modern applications, many contemporary problems involve high dimensional datum x ∈ R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sup> whose entries often lie in a union of subspaces and the goal is to find out which entries of x match with a particular subspace U, classically called matched subspace detection. Consequently, entries that match with one subspace are considered as inliers w.r.t the subspace while all other entries are considered as outliers. Proportion of outliers relative to each subspace varies based on the degree of coordinates from subspaces. This problem is a combinatorial NP-hard in nature and has been immensely studied in recent years. Existing approaches can solve the problem when outliers are sparse. However, if outliers are abundant or in other words if x contains coordinates from a fair amount of subspaces, this problem can't be solved with acceptable accuracy or within a reasonable amount of time. This paper proposes a twostage approach called Greedy Linear Integer Mixed Programmed Selector (GLIMPS) for this abundant-outliers setting, which combines a greedy algorithm and mixed integer formulation and can tolerate over 80% outliers, outperforming the state-ofthe-art.
Year
DOI
Venue
2019
10.1109/ALLERTON.2019.8919913
2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
Keywords
Field
DocType
super robust matched subspace detection,Greedy Mixed-Integer approach,Greedy Linear Integer Mixed Programmed Selector,particular subspace U,data acquisition
Integer,Signal processing,Combinatorics,Geodetic datum,Mathematical optimization,Subspace topology,Computer science,Outlier,Greedy algorithm,Linear subspace,Robustness (computer science)
Conference
ISSN
ISBN
Citations 
2474-0195
978-1-7281-3152-8
0
PageRank 
References 
Authors
0.34
25
2
Name
Order
Citations
PageRank
Md. Mahmudur Rahman11716.00
Daniel L. Pimentel-Alarcón201.35