Sparse approximations for kernel learning vector quantization
نویسندگان
چکیده
Various prototype based learning techniques have recently been extended to similarity data by means of kernelization. While stateof-the-art classification results can be achieved this way, kernelization loses one important property of prototype-based techniques: a representation of the solution in terms of few characteristic prototypes which can directly be inspected by experts. In this contribution, we introduce several different ways to obtain sparse representations for kernel learning vector quantization and compare its efficiency and performance in connection to the underlying data characteristics in diverse benchmark scenarios.
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