Sparse approximations for kernel learning vector quantization
Sparse approximations for kernel learning vector quantization
复制标题
核学习矢量量化的稀疏近似
DOI：
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复制
发表时间：
2013
期刊：
影响因子：
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通讯作者：
B. Hammer
中科院分区：
文献类型：
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作者：
Daniela Hofmann;B. Hammer
.各种基于原型的学习技术最近已扩展到相似性数据的核化。虽然可以通过这种方式实现最先进的分类结果，但核化失去了基于原型的技术的一个重要属性：解决方案的表示可以由专家直接检查的少数特征原型。在这篇文章中，我们介绍了几种不同的方法来获得核学习矢量量化的稀疏表示，并比较其效率和性能与不同基准场景中的底层数据特征。
. Various prototype based learning techniques have recently been extended to similarity data by means of kernelization. While state-of-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.
影响因子：
7.8
作者：
Bunte, Kerstin;Schneider, Petra;Biehl, Michael
通讯作者：
Biehl, Michael