NDLI: Empirical mode decomposition based sparse dictionary learning with application to signal classification

Content Provider	IEEE Xplore Digital Library
Author	Kaleem, M. Guergachi, A. Krishnan, S.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, ON, Canada (Kaleem, M.; Guergachi, A.; Krishnan, S.)
Abstract	This paper will present a novel empirical framework for dictionary learning where the dictionary is learned from the data to be analyzed, rather than using a pre-defined basis. A dictionary formation and learning algorithm is presented, which learns sparse dictionaries, where sparsity is understood in terms of the small number of dictionary atoms compared to the signal dimensions. An initial dictionary is formed using training signals of different classes, where the dictionary atoms consist of intrinsic mode functions obtained as a result of decomposing the training signals using empirical mode decomposition. A dictionary learning algorithm trains this dictionary which results in a significant reduction in the size of the learned dictionary. The learned dictionary can be applied to signal classification, whereby coefficients of orthogonal projections of test signals against the learned dictionary are used as features to classify the test signals into different classes. We also show that the learned dictionary allows calculation of the coefficient vector based on sparse representation of test signals, which can also be used as a feature vector. Although the framework is not formulated as reconstructive, or combined reconstructive and discriminative dictionary learning, its efficacy in signal classification is demonstrated using real-life EEG signals.
Starting Page	18
Ending Page	23
File Size	292376
Page Count	6
File Format	PDF
e-ISBN	9781479916160
DOI	10.1109/DSP-SPE.2013.6642558
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Rain Vectors Training Accuracy Electroencephalography Image reconstruction classification empirical mode decomposition dictionary learning sparse dictionary
Content Type	Text
Resource Type	Article

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