NDLI: Matching samples of multiple views

Content Provider	Springer Nature Link
Author	Tripathi, Abhishek Klami, Arto Orešič, Matej Kaski, Samuel
Copyright Year	2010
Abstract	Multi-view learning studies how several views, different feature representations, of the same objects could be best utilized in learning. In other words, multi-view learning is analysis of co-occurrence data, where the observations are co-occurrences of samples in the views. Standard multi-view learning such as joint density modeling cannot be done in the absence of co-occurrence, when the views are observed separately and the identities of objects are not known. As a practical example, joint analysis of mRNA and protein concentrations requires mapping between genes and proteins. We introduce a data-driven approach for learning the correspondence of the observations in the different views, in order to enable joint analysis also in the absence of known co-occurrence. The method finds a matching that maximizes statistical dependency between the views, which is particularly suitable for multi-view methods such as canonical correlation analysis which has the same objective. We apply the method to translational metabolomics, to identify differences and commonalities in metabolic processes in different species or tissues. The metabolite identities and roles in the different species are not generally known, and it is necessary to search for a matching. In this paper we show, using different metabolomics measurement batches as the views so that the ground truth is known, that the metabolite identities can be reliably matched by a consensus of several matching solutions.
Starting Page	300
Ending Page	321
Page Count	22
File Format	PDF
ISSN	13845810
Journal	Data Mining and Knowledge Discovery
Volume Number	23
Issue Number	2
e-ISSN	1573756X
Language	English
Publisher	Springer US
Publisher Date	2010-11-28
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bipartite matching Canonical correlation Consensus matching Co-occurrence data Multi-view learning Computing Methodologies Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Statistics Data Mining and Knowledge Discovery Information Storage and Retrieval Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Computer Science Applications

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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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