Regularization in relevance learning vector quantization using l1-norms
نویسندگان
چکیده
We propose in this contribution a method for l1-regularization in prototype based relevance learning vector quantization (LVQ) for sparse relevance pro les. Sparse relevance pro les in hyperspectral data analysis fade down those spectral bands which are not necessary for classi cation. In particular, we consider the sparsity in the relevance pro le enforced by LASSO optimization. The latter one is obtained by a gradient learning scheme using a di erentiable parametrized approximation of the l1-norm, which has an upper error bound. We extend this regularization idea also to the matrix learning variant of LVQ as the natural generalization of relevance learning.
منابع مشابه
Regularization in Relevance Learning Vector Quantization Using l one Norms
We propose in this contribution a method for l1-regularization in prototype based relevance learning vector quantization (LVQ) for sparse relevance profiles. Sparse relevance profiles in hyperspectral data analysis fade down those spectral bands which are not necessary for classification. In particular, we consider the sparsity in the relevance profile enforced by LASSO optimization. The latter...
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