NDLI: Efficient dictionary learning via very sparse random projections

Content Provider	IEEE Xplore Digital Library
Author	Pourkamali-Anaraki, F. Becker, S. Hughes, S.M.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr., Comput., & Energy Eng., Univ. of Colorado at Boulder, Boulder, CO, USA (Pourkamali-Anaraki, F.; Hughes, S.M.) \|\| Dept. of Appl. Math., Univ. of Colorado at Boulder, Boulder, CO, USA (Becker, S.)
Abstract	Performing signal processing tasks on compressive measurements of data has received great attention in recent years. In this paper, we extend previous work on compressive dictionary learning by showing that more general random projections may be used, including sparse ones. More precisely, we examine compressive K-means clustering as a special case of compressive dictionary learning and give theoretical guarantees for its performance for a very general class of random projections. We then propose a memory and computation efficient dictionary learning algorithm, specifically designed for analyzing large volumes of high-dimensional data, which learns the dictionary from very sparse random projections. Experimental results demonstrate that our approach allows for reduction of computational complexity and memory/data access, with controllable loss in accuracy.
Starting Page	478
Ending Page	482
File Size	364242
Page Count	5
File Format	PDF
ISBN	9781467373531
DOI	10.1109/SAMPTA.2015.7148937
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Dictionaries Image coding Accuracy Signal processing algorithms Signal processing Sparse matrices
Content Type	Text
Resource Type	Article

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