Robust Low-Complexity methods for matrix column outlier identification

Xingguo Li, Jarvis Haupt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper examines the problem of locating outlier columns in a large, otherwise low-rank matrix, in settings where the data are noisy, or where the overall matrix has missing elements. We propose an efficient randomized two-step inference framework, and establish sufficient conditions on the required sample complexities under which these methods succeed (with high probability) in accurately locating the outliers for each task. Comprehensive numerical experimental results are provided to validate the theoretical bounds and demonstrate the computational efficiency of the proposed algorithm.

Original languageEnglish (US)
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538612514
DOIs
StatePublished - Mar 9 2018
Event7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 - Curacao
Duration: Dec 10 2017Dec 13 2017

Publication series

Name2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
Volume2017-December

Conference

Conference7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
CityCuracao
Period12/10/1712/13/17

Bibliographical note

Funding Information:
ACKNOWLEDGMENT The authors acknowledge support from NSF Award CCF-1217751 and the DARPA YFA, Award N66001-14-1-4047.

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