NDLI: Design of augmented dictionary for sparse representation based on neural network

Content Provider	IEEE Xplore Digital Library
Author	Hui Qv Jihao Yin DiMarzio, C.A.
Copyright Year	2015
Description	Author affiliation: Sch. of Astronaut., Beihang Univ., Beijing, China (Hui Qv; Jihao Yin) \|\| Electr. & Comput. Eng, Northeastern Univ., Boston, MA, USA (DiMarzio, C.A.)
Abstract	An efficient and flexible dictionary designing algorithm is proposed for sparse and redundant signal representation. The proposed Augmented Dictionary (AD) is based on a new dictionary model with an augmented form compared to the conventional model. With this model, we can bridge the gap between the classic dictionary learning approaches, which have general structure yet lack computational efficiency, and the artificial neural network theory, which has potential high parallel computational efficiency but poor universality of structure. In this paper, we discuss the advantages of augmented dictionary, and interpret how the augmented dictionary can be trained with labeled samples. The proposed neural network based augmented dictionary designing method enjoys some important features, such as high accuracy, strong robustness and desired computational efficiency. As a demonstration of these benefits, we present high-quality hyperspectral image classification results based on the new algorithm.
Starting Page	389
Ending Page	392
File Size	396496
Page Count	4
File Format	PDF
e-ISBN	9781479979295
DOI	10.1109/IGARSS.2015.7325782
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-26
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Training Neural networks Matching pursuit algorithms Hyperspectral imaging Accuracy Algorithm design and analysis classification dictionary learning neural network hyperspectral
Content Type	Text
Resource Type	Article

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