Abstract
We propose a functional relevance learning for learning vector quantization of functional data. The relevance profile is taken as a superposition of a set of basis functions depending on only a few parameters compared to standard relevance learning. Moreover, the sparsity of the superposition is achieved by an entropy based penalty function forcing sparsity.
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Villmann, T., Kästner, M. (2011). Sparse Functional Relevance Learning in Generalized Learning Vector Quantization. In: Laaksonen, J., Honkela, T. (eds) Advances in Self-Organizing Maps. WSOM 2011. Lecture Notes in Computer Science, vol 6731. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21566-7_8
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DOI: https://doi.org/10.1007/978-3-642-21566-7_8
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