Title	What Is Beyond Sparse Coding?
Creator	Wu, Yingnian
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2014-02-12
Description	Many types of data such as natural images admit sparse representations by redundant dictionaries of basis functions (or regressors), and these dictionaries can either be designed or learned from training data. However, it is still unclear how to go beyond sparsity and continue to learn structures behind the sparse representations. In this talk, I shall review some recent progresses and the major issues and difficulties that need to be addressed. I shall also present our own recent work that seeks to learn dictionaries of compositional patterns in the sparse representations. Based on joint work with Jianwen Xie, Wenze Hu and Song-Chun Zhu.
Extent	52 minutes
Subject	Mathematics; Statistics; Biology and other natural sciences; Applied statistics
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: University of California, Los Angeles
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2014-08-07
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivs 2.5 Canada
DOI	10.14288/1.0043889
URI	http://hdl.handle.net/2429/49840
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/2.5/ca/
Aggregated Source Repository	DSpace

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