Dependency detection with similarity constraints

Details Dependency detection with similarity constraints Dependency detection with similarity constraints

2011-01-31

arxiv.org/abs/1101.5919v1

In T{\"u}lay Adali, Jocelyn Chanussot, Christian Jutten, and Jan Larsen, editors, Proceedings of the 2009 IEEE International W

Statistics

Machine Learning

9 pages, 3 figures. Appeared in proceedings of the 2009 IEEE International Workshop on Machine Learning for Signal Processing

Scientific paper

Unsupervised two-view learning, or detection of dependencies between two paired data sets, is typically done by some variant of canonical correlation analysis (CCA). CCA searches for a linear projection for each view, such that the correlations between the projections are maximized. The solution is invariant to any linear transformation of either or both of the views; for tasks with small sample size such flexibility implies overfitting, which is even worse for more flexible nonparametric or kernel-based dependency discovery methods. We develop variants which reduce the degrees of freedom by assuming constraints on similarity of the projections in the two views. A particular example is provided by a cancer gene discovery application where chromosomal distance affects the dependencies between gene copy number and activity levels. Similarity constraints are shown to improve detection performance of known cancer genes.

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