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Semisupervised Clustering Based on Conditional Distributions in an AuxiliarySpace

机译：基于辅助空间条件分布的半监督聚类

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The authors study the problem of learning groups or categories that are local inthe continuous primary space, but homogeneous by the distributions of an associated auxiliary random variable over a discrete auxiliary space. Assuming variation in the auxiliary space is meaningful, categories will emphasize similarly meaningful aspects of the primary space. From a data set consisting of pairs of primary and auxiliary items, the categories are learned by minimizing a Kullback-Leibler divergence-based distortion between (implicitly estimated) distributions of the auxiliary data, conditioned on the primary data. Still, the categories are solely defined in terms of the primary space. An on-line algorithm resembling the traditional Hebb-type competitive learning is introduced for learning the categories. Minimizing the distortion criterion turns out to be equivalent to maximizing the mutual information between the categories and the auxiliary data.

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