Visible to the public "Outlier identification via randomized adaptive compressive sampling"Conflict Detection Enabled

Title"Outlier identification via randomized adaptive compressive sampling"
Publication TypeConference Paper
Year of Publication2015
AuthorsX. Li, J. D. Haupt
Conference Name2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date PublishedApril
PublisherIEEE
ISBN Number978-1-4673-6997-8
Accession Number15362159
KeywordsAdaptive and compressive sensing, adaptive compressive sampling, adaptive sensing, automated surveillance, compressed sensing, Computer vision, Imaging, outlier columns, outlier identification, pubcrawl, robust PCA, saliency map estimation tasks, Silicon
Abstract

This paper examines the problem of locating outlier columns in a large, otherwise low-rank, matrix. We propose a simple two-step adaptive sensing and inference approach and establish theoretical guarantees for its performance. Our results show that accurate outlier identification is achievable using very few linear summaries of the original data matrix - as few as the squared rank of the low-rank component plus the number of outliers, times constant and logarithmic factors. We demonstrate the performance of our approach experimentally in two stylized applications, one motivated by robust collaborative filtering tasks, and the other by saliency map estimation tasks arising in computer vision and automated surveillance.

URLhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7178582&isnumber=7177909
DOI10.1109/ICASSP.2015.7178582
Citation Key7178582