Fuzzy feature weighting techniques for vector quantisation

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

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
ISBN (Electronic)9781424469215
ISBN (Print)9781424469192, 9781424481262
DOIs
Publication statusPublished - 2010
Event2010 IEEE World Congress on Computational Intelligence (FUZZ-IEEE 2010) - Barcelona, Barcelona, Spain
Duration: 18 Jul 201023 Jul 2010

Conference

Conference2010 IEEE World Congress on Computational Intelligence (FUZZ-IEEE 2010)
CountrySpain
CityBarcelona
Period18/07/1023/07/10

Fingerprint

Vector quantization
Pattern recognition

Cite this

Tran, D., Ma, W., & Sharma, D. (2010). Fuzzy feature weighting techniques for vector quantisation. In 2010 IEEE World Congress on Computational Intelligence United States: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/FUZZY.2010.5584420
Tran, Dat ; Ma, Wanli ; Sharma, Dharmendra. / Fuzzy feature weighting techniques for vector quantisation. 2010 IEEE World Congress on Computational Intelligence. United States : IEEE, Institute of Electrical and Electronics Engineers, 2010.
@inproceedings{9a1436d792854998b7595aa210cae002,
title = "Fuzzy feature weighting techniques for vector quantisation",
abstract = "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",
author = "Dat Tran and Wanli Ma and Dharmendra Sharma",
year = "2010",
doi = "10.1109/FUZZY.2010.5584420",
language = "English",
isbn = "9781424469192",
booktitle = "2010 IEEE World Congress on Computational Intelligence",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
address = "United States",

}

Tran, D, Ma, W & Sharma, D 2010, Fuzzy feature weighting techniques for vector quantisation. in 2010 IEEE World Congress on Computational Intelligence. IEEE, Institute of Electrical and Electronics Engineers, United States, 2010 IEEE World Congress on Computational Intelligence (FUZZ-IEEE 2010), Barcelona, Spain, 18/07/10. https://doi.org/10.1109/FUZZY.2010.5584420

Fuzzy feature weighting techniques for vector quantisation. / Tran, Dat; Ma, Wanli; Sharma, Dharmendra.

2010 IEEE World Congress on Computational Intelligence. United States : IEEE, Institute of Electrical and Electronics Engineers, 2010.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

T1 - Fuzzy feature weighting techniques for vector quantisation

AU - Tran, Dat

AU - Ma, Wanli

AU - Sharma, Dharmendra

PY - 2010

Y1 - 2010

N2 - 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

AB - 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

U2 - 10.1109/FUZZY.2010.5584420

DO - 10.1109/FUZZY.2010.5584420

M3 - Conference contribution

SN - 9781424469192

SN - 9781424481262

BT - 2010 IEEE World Congress on Computational Intelligence

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - United States

ER -

Tran D, Ma W, Sharma D. Fuzzy feature weighting techniques for vector quantisation. In 2010 IEEE World Congress on Computational Intelligence. United States: IEEE, Institute of Electrical and Electronics Engineers. 2010 https://doi.org/10.1109/FUZZY.2010.5584420