NDLI: Learning a discriminative dictionary for locality constrained coding and sparse representation

Content Provider	IEEE Xplore Digital Library
Author	Jin Bin Zhang Jing Yang Zhiyong
Copyright Year	2014
Description	Author affiliation: Seven Dept., Naval Aeronaut. & Astronaut. Univ., Yantai, China (Yang Zhiyong) \|\| Armament Branch, NED, Beijing, China (Jin Bin) \|\| Dept. of Electron. & Inf. Eng., Naval Aeronaut. & Astronaut. Univ., Yantai, China (Zhang Jing)
Abstract	Motivated by image reconstruction, sparse representation based classification (SRC) and locality-constrained linear coding (LLC) have been shown to be effective methods for applications. In this paper, we propose a new dictionary learning and sparse representation approach. During sparse coding step, we incorporate locality on representation samples, which preserves local data structure, resulting in improved classification. In dictionary learning step, a `discriminative' sparse coding error criterion and an `optimal' classification performance criterion are added into the objective function for better discriminating power. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse representation techniques for face and SAR recognition.
Starting Page	1
Ending Page	6
File Size	223112
Page Count	6
File Format	PDF
e-ISBN	9781479926961
DOI	10.1109/ICCCNT.2014.6963006
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Dictionaries Databases Target recognition Data locality Linear programming Sparse representation Encoding Classification algorithms Image reconstruction Dictionary learning
Content Type	Text
Resource Type	Article

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