PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Using Dependencies to Pair Samples for Multi-View Learning
Abhishek Tripathi, Arto Klami and Samuel Kaski
In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009), 19-24, April 2009, Taipei, Taiwan.

Abstract

Several data analysis tools such as (kernel) canonical correlation analysis and various multi-view learning methods require paired observations in two data sets. We study the problem of inferring such pairing for data sets with no known one-to-one pairing. The pairing is found by an iterative algorithm that alternates between searching for feature representations that reveal statistical dependencies between the data sets, and finding the best pairs for the samples. The method is applied on pairing probe sets of two different microarray platforms.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:4473
Deposited By:Abhishek Tripathi
Deposited On:13 March 2009