NDLI: Noise reduction in chaotic multi-dimensional time series using dictionary learning

Content Provider	IET Digital Library
Author	Sun, Jiancheng
Abstract	Chaotic multi-dimensional time series (MDTS) exist in some fields such as stock markets and life sciences. To effectively extract the desired information from the measured MDTS, it is important to preprocess data to reduce noise. On the basis of dictionary learning, a method to remove noise is proposed, and the proposed approach is shown to be very effective in the case of MDTS. An MDTS is first considered as a whole, namely an image, and then the method is applied on it. Compared with traditional methods, the proposed approach can utilise the information among the different dimensional time series to improve noise reduction. Using the Lorenz data superimposed by the Gaussian noise as an example, the simulation results have validated the mathematical framework and the performance.
Starting Page	1635
Ending Page	1637
Page Count	3
ISSN	00135194
Volume Number	50
e-ISSN	1350911X
Issue Number	Issue 22, Oct (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/50/22
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2014.1757
Journal	Electronics Letters
Publisher Date	2014-10-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Chaos Chaotic Multidimensional Time Series Computer Vision And Image Processing Technique Dictionary Learning Feature Extraction Image Denoising Image Information Extraction Image Recognition Knowledge Engineering Technique Learning in AI Mathematical Framework MDTS Noise Reduction Statistics Time Series
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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