Title :
A class of order statistics learning vector quantizers
Author :
Pitas, I. ; Kotropoulos, C. ; Nikolaidis, N. ; Yang, R. ; Gabbouj, M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Thessaloniki Univ., Greece
fDate :
30 May-2 Jun 1994
Abstract :
A novel class of Learning Vector Quantizers (LVQs) based on multivariate order statistics is proposed in order to overcome the drawback that the estimators for obtaining the reference vectors in LVQ do not have robustness either against erroneous choices for the winner vector or against the outliers that may exist in vector-valued observations. The performance of the proposed variants of LVQ is demonstrated by experiments. In the case of marginal median LVQ, its asymptotic properties are derived as well
Keywords :
learning (artificial intelligence); neural nets; statistics; vector quantisation; asymptotic properties; learning vector quantizers; marginal median LVQ; multivariate order statistics; Artificial neural networks; Error correction codes; Laboratories; Network topology; Neural networks; Robustness; Signal processing; Signal processing algorithms; Statistics; Vectors;
Conference_Titel :
Circuits and Systems, 1994. ISCAS '94., 1994 IEEE International Symposium on
Conference_Location :
London
Print_ISBN :
0-7803-1915-X
DOI :
10.1109/ISCAS.1994.409607
