Fuzzy feature weighting techniques for vector quantisation

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

15 Downloads (Pure)


Vector quantization (VQ) is a simple but effective modelling technique in pattern recognition. VQ employs a clustering technique to convert a feature vector set in to a cluster center set to model the feature vector set. Some clustering techniques have been applied to improve VQ. However VQ is not always effective because data features are treated equally although their importance may not be the same. Some automated feature weighting techniques have been proposed to overcome this drawback. This paper reviews those weighting techniques and proposes a general scheme for selecting any pair of clustering and feature weighting techniques to form a fuzzy feature weighting-based VQ modelling technique. Besides the current techniques, a number of new feature weighting-based VQ techniques is proposed and their evaluations are also presented
Original languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781424469215
ISBN (Print)9781424469192, 9781424481262
Publication statusPublished - 2010
Event2010 IEEE World Congress on Computational Intelligence (FUZZ-IEEE 2010) - Barcelona, Barcelona, Spain
Duration: 18 Jul 201023 Jul 2010


Conference2010 IEEE World Congress on Computational Intelligence (FUZZ-IEEE 2010)


Dive into the research topics of 'Fuzzy feature weighting techniques for vector quantisation'. Together they form a unique fingerprint.

Cite this