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TSP
2008
151views more  TSP 2008»
13 years 7 months ago
Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors
The rapid developing area of compressed sensing suggests that a sparse vector lying in a high dimensional space can be accurately and efficiently recovered from only a small set of...
Moshe Mishali, Yonina C. Eldar
JCDL
2004
ACM
146views Education» more  JCDL 2004»
14 years 1 months ago
Enhancing digital libraries with TechLens+
The number of research papers available is growing at a staggering rate. Researchers need tools to help them find the papers they should read among all the papers published each y...
Roberto Torres, Sean M. McNee, Mara Abel, Joseph A...
KDD
2009
ACM
198views Data Mining» more  KDD 2009»
14 years 8 months ago
Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data
All Netflix Prize algorithms proposed so far are prohibitively costly for large-scale production systems. In this paper, we describe an efficient dataflow implementation of a coll...
Srivatsava Daruru, Nena M. Marin, Matt Walker, Joy...
TCSV
2002
132views more  TCSV 2002»
13 years 7 months ago
A study of real-time packet video quality using random neural networks
An important and unsolved problem today is the automatic quantification of the quality of video flows transmitted over packet networks. In particular, the ability to perform this ...
Samir Mohamed, Gerardo Rubino
ICASSP
2009
IEEE
14 years 2 months ago
Sparse LMS for system identification
We propose a new approach to adaptive system identification when the system model is sparse. The approach applies the ℓ1 relaxation, common in compressive sensing, to improve t...
Yilun Chen, Yuantao Gu, Alfred O. Hero III