NDLI: Using the Karhunen-Loeve expansion for feature extraction on small sample sets

Content Provider	IEEE Xplore Digital Library
Author	Pannekamp, J. Schmidberger, E. Kischkat, R.
Copyright Year	1998
Description	Author affiliation: Fraunhofer-Inst. fur Produktionstech. und Autom., Stuttgart, Germany (Pannekamp, J.)
Abstract	A general task in the field of signal processing and control consists in representing signals by a small number of features, i.e. the mapping of an n-dimensional signal onto an m-dimensional feature vector. Examples for widely used features are the position of maxima, minima, gradients or certain integral values of the signal curve, although it is obvious that the original curves can hardly be reconstructed from these features. The Karhunen-Loeve (K-L) expansion on the other hand provides the means to approximate the n-dimensional sample vectors of a random distribution by m features such that the mean-square error is minimized. However, these m basis vectors of the K-L-expansion may not be appropriate for representing signals which are not distributed according to the covariance matrix of the given sample set. In this paper, a method is presented which improves the representation of signals with a distribution deviating from the given distribution by reducing the dependence of the feature vectors on the sample set. Feature vectors spanning a subspace of slowly varying functions are combined with basis vectors of the K-L-expansion in order to increase the robustness of the features.
Starting Page	1582
Ending Page	1586
File Size	327873
Page Count	5
File Format	PDF
ISBN	0780345037
DOI	10.1109/IECON.1998.722895
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-08-31
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Vectors Signal processing Covariance matrix Signal representations Process control Data mining Signal mapping Robust control Injection molding
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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