Kernelized Infomax Clustering
Kernelized Infomax Clustering
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核化 Infomax 聚类
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
D. Barber
中科院分区：
文献类型：
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作者：
F. Agakov;D. Barber
我们提出了一种简单的信息论软聚类方法，该方法基于最大化未知聚类标签y和训练模式x之间关于特定约束编码分布的参数的互信息I(x，y)。约束的选择使得如果图案位于特征空间中的特定未知向量附近，则它们可能被类似地聚集。该方法可以方便地用于学习与核化编码器的学习参数相对应的最优亲和力矩阵。该过程不需要计算Gram矩阵的特征值，这使得它对于大规模数据集的聚类具有潜在的吸引力。
We propose a simple information-theoretic approach to soft clustering based on maximizing the mutual information I(x,y) between the unknown cluster labels y and the training patterns x with respect to parameters of specifically constrained encoding distributions. The constraints are chosen such that patterns are likely to be clustered similarly if they lie close to specific unknown vectors in the feature space. The method may be conveniently applied to learning the optimal affinity matrix, which corresponds to learning parameters of the kernelized encoder. The procedure does not require computations of eigenvalues of the Gram matrices, which makes it potentially attractive for clustering large data sets.