Using dependencies to pair samples for multi-view learning
No Thumbnail Available
Faculty of Information and Natural Sciences | D4 Julkaistu kehittämis- tai tutkimusraportti taikka -selvitys
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
TKK reports in information and computer science, 8
AbstractSeveral 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.
canonical correlation, co-occurrence data, dependency, multi-view learning