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122
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ICASSP
2009
IEEE
15 years 10 days ago
On the recovery of nonnegative sparse vectors from sparse measurements inspired by expanders
This paper studies compressed sensing for the recovery of non-negative sparse vectors from a smaller number of measurements than the ambient dimension of the unknown vector. We fo...
M. Amin Khajehnejad, Babak Hassibi
82
Voted
ICASSP
2011
IEEE
14 years 6 months ago
Steady-state analysis of the NLMS algorithm with reusing coefficient vector and a method for improving its performance
The reuse of past coefficient vectors of the NLMS for reducing the steady-state MSD in a low signal-to-noise ratio (SNR) was proposed recently. Its convergence analysis has not b...
Seong-Eun Kim, Jae-Woo Lee, Woo-Jin Song
NLDB
2007
Springer
15 years 8 months ago
Cross-Lingual Information Retrieval by Feature Vectors
This paper investigates query translation in cross-lingual information retrieval, especially the challenges caused by ambiguity and polysemi. We base our ideas on feature vectors a...
Jeanine Lilleng, Stein L. Tomassen
109
Voted
CASES
2003
ACM
15 years 7 months ago
Vectorizing for a SIMdD DSP architecture
The Single Instruction Multiple Data (SIMD) model for fine-grained parallelism was recently extended to support SIMD operations on disjoint vector elements. In this paper we demon...
Dorit Naishlos, Marina Biberstein, Shay Ben-David,...
CORR
2011
Springer
148views Education» more  CORR 2011»
14 years 9 months ago
How well can we estimate a sparse vector?
The estimation of a sparse vector in the linear model is a fundamental problem in signal processing, statistics, and compressive sensing. This paper establishes a lower bound on t...
Emmanuel J. Candès, Mark A. Davenport