Using dependencies to pair samples for multi-view learning

Author

Tripathi, Abhishek ; Klami, Arto ; Kaski, Samuel

Author_Institution

Dept. of Comput. Sci., Univ. of Helsinki, Helsinki

fYear

2009

fDate

19-24 April 2009

Firstpage

1561

Lastpage

1564

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.

Keywords

data analysis; iterative methods; learning (artificial intelligence); statistical analysis; data analysis tools; feature representations; iterative algorithm; kernel canonical correlation analysis; microarray platforms; multiview learning; one-to-one pairing; pairing probe sets; statistical dependency; Computer science; Data analysis; Information retrieval; Iterative algorithms; Kernel; Learning systems; Machine learning; Probes; Semiconductor device measurement; Text mining; canonical correlation; co-occurrence data; dependency; multi-view learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on

Conference_Location

Taipei

ISSN

1520-6149

Print_ISBN

978-1-4244-2353-8

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2009.4959895

Filename

4959895