On surrogate supervision multi-view learning

Graduate Thesis Or Dissertation

Public Deposited

Citeable URL: https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/j098zd482

Descriptions

Attribute Name	Values
Creator	Jin, Gaole
Abstract	Data can be represented in multiple views. Traditional multi-view learning methods (i.e., co-training, multi-task learning) focus on improving learning performance using information from the auxiliary view, although information from the target view is sufficient for learning task. However, this work addresses a semi-supervised case of multi-view learning, the surrogate supervision multi-view learning, where labels are available on limited views and a classifier is obtained on the target view where labels are missing. In surrogate multi-view learning, one cannot obtain a classifier without information from the auxiliary view. To solve this challenging problem, we propose discriminative and generative approaches.
License	All rights reserved
Resource Type	Masters Thesis
Date Available	2013-04-03T21:58:20+00:00
Date Issued	2012-12-03
Degree Level	Master's
Degree Name	Master of Science (M.S.)
Degree Field	Electrical and Computer Engineering
Degree Grantor	Oregon State University
Commencement Year	2013
Advisor	Raich, Raviv
Committee Member	Smart, Bill Todorovic, Sinisa Fern, Xiaoli
Academic Affiliation	Electrical Engineering and Computer Science
Non-Academic Affiliation	Oregon State University. Graduate School
Subject	Supervised learning (Machine learning)
Rights Statement	In Copyright
Publisher	Oregon State University
Peer Reviewed	No
Language	English [eng]
Replaces	http://hdl.handle.net/1957/37997

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