Functional relevance learning in generalized learning vector quantization

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• 作者：Marika Kä ; stner^a  ; [Author Vitae] ; Barbara Hammer^b  ; [Author Vitae] ; Michael Biehl^c  ; [Author Vitae] ; Thomas Villmann^a ; ^{thomas.villmann@hs-mittweida.de} ; ^{villmann@hs-mittweida.de}  ; [Author Vitae]
• 关键词：Functional vector quantization ; Relevance learning ; Feature weighting and selection ; Sparse models
• 刊名：Neurocomputing
• 出版年：2012
• 期刊代码：95_09252312
• 类别：cp
• 出版时间：1 August, 2012
• 卷：90
• 期：Complete
• 页码：85-95
• 文件大小：1836 K

摘要

Relevance learning in learning vector quantization is a central paradigm for classification task depending feature weighting and selection. We propose a functional approach to relevance learning for high-dimensional functional data. For this purpose we compose the relevance profile by a superposition of only a few parametrized basis functions taking into account the functional character of the data. The number of these parameters is usually significantly smaller than the number of relevance weights in standard relevance learning, which is the number of data dimensions. Thus, instabilities in learning are avoided and an inherent regularization takes place. In addition, we discuss strategies to obtain sparse relevance models for further model optimization.