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K-SVD based Periodicity Dictionary Learning

Kulkarni, Pranav and Vaidyanathan, P. P. (2020) K-SVD based Periodicity Dictionary Learning. In: 2020 54th Asilomar Conference on Signals, Systems, and Computers. IEEE , Piscataway, NJ, pp. 1333-1337. ISBN 978-0-7381-3126-9.

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It has recently been shown that periodicity in discrete-time data can be analyzed using Ramanujan sums and associated dictionaries. This paper explores the role of dictionary learning methods in the context of period estimation and periodic signal representation using dictionaries. It is shown that a well-known dictionary learning algorithm, namely K-SVD, is able to learn Ramanujan and Farey periodicity dictionaries from the noisy, sparse coefficient data generated from them without imposing any periodicity structure in the learning stage. This similarity between the learned dictionary and the underlying original periodicity dictionary reaffirms the power of the K-SVD in predicting the right dictionary from data without explicit application-specific constraints. The paper also examines how the choice of different parameter values affect the similarity of the learned dictionary to the underlying dictionary. Two versions of K-SVD along with different initializations are analyzed for their effect on representation and denoising error for the data.

Item Type:Book Section
Related URLs:
URLURL TypeDescription
Kulkarni, Pranav0000-0002-1461-0948
Vaidyanathan, P. P.0000-0003-3003-7042
Additional Information:© 2020 IEEE. This work was supported in parts by the ONR grant N00014-18-1-2390, the NSF grant CCF-1712633, and the California Institute of Technology.
Funding AgencyGrant Number
Office of Naval Research (ONR)N00014-18-1-2390
Subject Keywords:Nested periodic dictionaries, dictionary learning, K-SVD, period estimation, denoising
Record Number:CaltechAUTHORS:20210622-213746336
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Official Citation:P. Kulkarni and P. P. Vaidyanathan, "K-SVD based Periodicity Dictionary Learning," 2020 54th Asilomar Conference on Signals, Systems, and Computers, 2020, pp. 1333-1337, doi: 10.1109/IEEECONF51394.2020.9443567
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109537
Deposited By: Tony Diaz
Deposited On:23 Jun 2021 18:55
Last Modified:23 Jun 2021 18:55

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