LVQ-FCV for missing value estimation and pattern classification

Author

Ikeda, Eriko ; Ichihashi, Hidetomo ; Nagasaka, Kazunori ; Miyoshi, Tetsuya

Author_Institution

Dept. of Ind. Eng., Osaka Prefecture Univ., Japan

Volume

6

fYear

1999

fDate

36342

Firstpage

4339

Abstract

This paper proposes a fuzzy LVQ with prototypes of linear varieties. Minimization of an objective function yields memberships of fuzzy clusters, principal components of the clusters and classification boundaries of LVQ type competitive learning. The proposed fuzzy c-varieties in this paper includes, within the Piccard iteration, a simple procedure for parameter estimation under missing data situations

Keywords

feedforward neural nets; fuzzy set theory; iterative methods; minimisation; multilayer perceptrons; parameter estimation; pattern classification; principal component analysis; unsupervised learning; vector quantisation; LVQ type competitive learning; LVQ-FCV; PCA; Piccard iteration; classification boundaries; fuzzy LVQ; fuzzy c-varieties; fuzzy cluster memberships; fuzzy multilayer feedforward neural net; missing data situations; missing value estimation; objective function minimization; parameter estimation; pattern classification; principal components; Clustering algorithms; Educational institutions; Industrial engineering; Lagrangian functions; Neurons; Parameter estimation; Pattern classification; Prototypes; Scattering; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.830866

Filename

830866