Paper Title:
Noise Fuzzy Learning Vector Quantization
  Abstract

Fuzzy learning vector quantization (FLVQ) benefits from using the membership values coming from fuzzy c-means (FCM) as learning rates and it overcomes several problems of learning vector quantization (LVQ). However, FLVQ is sensitive to noises because it is a FCM-based algorithm (FCM is sensitive to noises). Here, a new fuzzy learning vector quantization model, called noise fuzzy learning vector quantization (NFLVQ), is proposed to handle the noises sensitivity problem of FLVQ. NFLVQ integrates LVQ and generalized noise clustering (GNC), uses the membership values from GNC as learning rates and clusters data containing noisy data better than FLVQ. Experimental results show the better performances of NFLVQ.

  Info
Periodical
Key Engineering Materials (Volumes 439-440)
Edited by
Yanwen Wu
Pages
367-371
DOI
10.4028/www.scientific.net/KEM.439-440.367
Citation
X. H. Wu, B. Wu, J. W. Zhao, "Noise Fuzzy Learning Vector Quantization", Key Engineering Materials, Vols. 439-440, pp. 367-371, 2010
Online since
June 2010
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Price
$32.00
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