Limited Rank Matrix Learning, discriminative dimension reduction and visualization

We present an extension of the recently introduced Generalized Matrix Learning Vector Quantization algorithm. In the original scheme, adaptive square matrices of relevance factors parameterize a discriminative distance measure. We extend the scheme to matrices of limited rank corresponding to low-di...

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Vydané v:	Neural networks Ročník 26; s. 159 - 173
Hlavní autori:	Bunte, Kerstin, Schneider, Petra, Hammer, Barbara, Schleif, Frank-Michael, Villmann, Thomas, Biehl, Michael
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Kidlington Elsevier Ltd 01.02.2012 Elsevier
Predmet:	Adaptive metrics Algorithms Applied sciences Artificial Intelligence Classification Computer science; control theory; systems Data processing. List processing. Character string processing Dimension reduction Discriminant Analysis Exact sciences and technology Humans Learning Learning Vector Quantization Memory organisation. Data processing Pattern Recognition, Automated Software Visualization Visualization Dimension reduction Learning Vector Quantization Adaptive metrics Classification Cluster analysis Vector quantization Adaptive method Relevance Three dimensional representation Supervised learning Metric Learning algorithm Artificial intelligence
ISSN:	0893-6080, 1879-2782, 1879-2782
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