Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82295
Title: Sparse Sequential Generalization of K-means for dictionary training on noisy signals
Authors: Sahoo, Sujit Kumar
Makur, Anamitra
Keywords: Denoising
Sparse representation
Issue Date: 2016
Source: Sahoo, S. K., & Makur, A. (2016). Sparse Sequential Generalization of K-means for dictionary training on noisy signals. Signal Processing, 129, 62-66.
Series/Report no.: Signal Processing
Abstract: Noise incursion is an inherent problem in dictionary training on noisy samples. Therefore, enforcing a structural constrain on the dictionary will be useful for a stable dictionary training. Recently, a sparse dictionary with predefined sparsity has been proposed as a structural constraint. However, a fixed sparsity can become too rigid to adapt to the training samples. In order to address this issue, this article proposes a better solution through sparse Sequential Generalization of K-means (SGK). The beauty of the sparse-SGK is that it does not enforce a predefined rigid structure on the dictionary. Instead, a flexible sparse structure automatically emerges out of the training samples depending on the amount of noise. In addition, a variation of sparse-SGK using an orthogonal base dictionary is proposed for a quicker training. The advantages of sparse-SGK are demonstrated via 3-D image denoising. The experimental results confirm that sparse-SGK has better denoising performance and it takes lesser training time.
URI: https://hdl.handle.net/10356/82295
http://hdl.handle.net/10220/43516
ISSN: 0165-1684
DOI: 10.1016/j.sigpro.2016.05.036
Rights: © 2016 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Signal Processing, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.sigpro.2016.05.036].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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