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Tail inequalities for sums of random matrices that depend on the intrinsic dimension

Dublin Core		PKP Metadata Items	Metadata for this Document
1.	Title	Title of document	Tail inequalities for sums of random matrices that depend on the intrinsic dimension
2.	Creator	Author's name, affiliation, country	Daniel Hsu; Microsoft Research New England; United States
2.	Creator	Author's name, affiliation, country	Sham M. Kakade; Microsoft Research New England and University of Pennsylvania; United States
2.	Creator	Author's name, affiliation, country	Tong Zhang; Rutgers University; United States
3.	Subject	Discipline(s)
3.	Subject	Keyword(s)
4.	Description	Abstract	This work provides exponential tail inequalities for sums of random matrices that depend only on intrinsic dimensions rather than explicit matrix dimensions.  These tail inequalities are similar to the matrix versions of the Chernoff bound and Bernstein inequality except with the explicit matrix dimensions replaced by a trace quantity that can be small even when the explicit dimensions are large or infinite.  Some applications to covariance estimation and approximate matrix multiplication are given to illustrate the utility of the new bounds.
5.	Publisher	Organizing agency, location
6.	Contributor	Sponsor(s)
7.	Date	(YYYY-MM-DD)	2012-03-12
8.	Type	Status & genre	Peer-reviewed Article
8.	Type	Type
9.	Format	File format	PDF
10.	Identifier	Uniform Resource Identifier	http://ecp.ejpecp.org/article/view/1869
10.	Identifier	Digital Object Identifier	10.1214/ECP.v17-1869
11.	Source	Journal/conference title; vol., no. (year)	Electronic Communications in Probability; Vol 17
12.	Language	English=en	en
14.	Coverage	Geo-spatial location, chronological period, research sample (gender, age, etc.)
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