NDLI: Dependency detection with similarity constraints

Content Provider	IEEE Xplore Digital Library
Author	Lahti, L. Myllykangas, S. Knuutila, S. Kaski, S.
Copyright Year	2009
Description	Author affiliation: University of Helsinki and Helsinki University Central Hospital, Haartman Institute and HUSLAB, Department of Pathology, Finland (Knuutila, S.) \|\| Stanford University School of Medicine, Department of Medicine, Division of Oncology, and Stanford Genome Technology Center, Stanford University, USA (Myllykangas, S.) \|\| Helsinki University of Technology, Department of Information and Computer Science, PO Box 5400, FI-02015 TKK, Finland (Lahti, L.; Kaski, S.)
Abstract	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 freedomby 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.
Starting Page	1
Ending Page	6
File Size	2056597
Page Count	6
File Format	PDF
ISBN	9781424449477
ISSN	15512541
DOI	10.1109/MLSP.2009.5306192
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-01
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cancer Gene expression Genetic mutations Computer science Genomics Bioinformatics Multidimensional systems Vectors Particle measurements Bayesian methods
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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