Conference Paper

Correlation Clustering for Learning Mixtures of Canonical Correlation Models.

Source: DBLP

ABSTRACT This paper addresses the task of analyzing the correlation between two related domains X and Y. Our research is motivated by an Earth Science task that studies the rela- tionship between vegetation and precipitation. A standard statistical technique for such problems is Canonical Correla- tion Analysis (CCA). A critical limitation of CCA is that it can only detect linear correlation between the two domains that is globally valid throughout both data sets. Our ap- proach addresses this limitation by constructing a mixture of local linear CCA models through a process we name cor- relation clustering. In correlation clustering, both data sets are clustered simultaneously according to the data's corre- lation structure such that, within a cluster, domain X and domain Y are linearly correlated in the same way. Each clus- ter is then analyzed using the traditional CCA to construct local linear correlation models. We present results on both artificial data sets and Earth Science data sets to demon- strate that the proposed approach can detect useful correla- tion patterns, which traditional CCA fails to discover.

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