Conference paper

Publication year: 2006

Pages: 651-659

Journal: Neurocomputing

Volume number: 69

Issue number: 7-9

ISSN: 0925-2312

eISSN: 1872-8286

DOI Link: https://doi.org/10.1016/j.neucom.2005.12.004

Conference: 13th European Symposium on Artificial Neural Networks (ESANN)

Publisher: Elsevier

Abstract: 
A correlation-based similarity measure is derived for generalized relevance learning vector quantization (GRLVQ). The resulting GRLVQ-C classifier makes Pearson correlation available in a classification cost framework where data prototypes and global attribute weighting terms are adapted into directions of minimum cost function values. In contrast to the Euclidean metric, the Pearson correlation measure makes input vector processing invariant to shifting and scaling transforms, which is a valuable feature for dealing with functional data and with intensity observations like gene expression patterns. Two types of data measures are derived from Pearson correlation in order to make its benefits for data processing available in compact prototype classification models. Fast convergence and high accuracies are demonstrated for cDNA-array gene expression data. Furthermore, the automatic attribute weighting of GRLVQ-C is successfully used to rate the functional relevance of analyzed genes. (c) 2005 Elsevier B.V. All rights reserved.

Harvard Citation style: Strickert, M., Seiffert, U., Sreenivasulu, N., Weschke, W., Villmann, T. and Hammer, B. (2006) Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis, Neurocomputing, 69(7-9), pp. 651-659. https://doi.org/10.1016/j.neucom.2005.12.004

APA Citation style: Strickert, M., Seiffert, U., Sreenivasulu, N., Weschke, W., Villmann, T., & Hammer, B. (2006). Generalized relevance LVQ (GRLVQ) with correlation measures for gene expression analysis. Neurocomputing. 69(7-9), 651-659. https://doi.org/10.1016/j.neucom.2005.12.004