NDLI: Ordered weighted learning vector quantization and clustering algorithms

Content Provider	IEEE Xplore Digital Library
Author	Karayiannis, N.B.
Copyright Year	1998
Description	Author affiliation: Dept. of Electr. Eng., Houston Univ., TX, USA (Karayiannis, N.B.)
Abstract	This paper derives a broad variety of ordered weighted learning vector quantization (LVQ) algorithms. These algorithms map a set of feature vectors into a finite set of prototypes by adapting the weight vectors of a competitive neural network through an unsupervised learning process. The derivation of the proposed algorithms is accomplished by minimizing the average ordered weighted generalized mean of the Euclidean distances between the feature vectors and the prototypes using gradient descent. Under certain conditions, the proposed formulation results in ordered weighted clustering algorithms that can also be derived using alternating optimization. Moreover, existing LVQ and clustering algorithms are interpreted as special cases of the proposed formulation.
Starting Page	1388
Ending Page	1393
File Size	535700
Page Count	6
File Format	PDF
ISBN	078034863X
ISSN	10987584
DOI	10.1109/FUZZY.1998.686322
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-05-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vector quantization Clustering algorithms Minimization methods Equations Prototypes Neural networks Fuzzy sets Design engineering Unsupervised learning Algorithm design and analysis
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

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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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