NDLI: An Adaptive Projected Subgradient based algorithm for robust subspace tracking

Content Provider	IEEE Xplore Digital Library
Author	Chouvardas, S. Kopsinis, Y. Theodoridis, S.
Copyright Year	2014
Description	Author affiliation: Comput. Technol. Inst. & Press, Rio, Greece (Chouvardas, S.) \|\| Dept. of Inf. & Telecommun., Univ. of Athens, Athens, Greece (Kopsinis, Y.; Theodoridis, S.)
Abstract	In this paper, an Adaptive Projected Subgradient Method (APSM) based algorithm for robust subspace tracking is introduced. A properly chosen cost function is constructed at each time instance and the goal is to seek for points, which belong to the zero level set of this function; i.e., the set of points which score a zero loss. In each iteration, an outlier detection mechanism is employed, in order to conclude whether the current data vector contains outlier noise or not. Furthermore, a sparsity-promoting greedy algorithm is employed for the outlier vector estimation allowing the purification of the corrupted data from the outlier noise prior further processing. A theoretical analysis is carried out and experiments within the context of robust subspace estimation exhibit the enhanced performance of the proposed scheme compared to a recently developed state of the art algorithm.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	5497
Ending Page	5501
File Size	205098
Page Count	5
File Format	PDF
ISBN	9781479928934
DOI	10.1109/ICASSP.2014.6854654
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vectors Robustness Noise Signal processing algorithms Cost function Algorithm design and analysis Estimation Greedy Algorithms Robust Subspace Tracking APSM
Content Type	Text
Resource Type	Article

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