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ICASSP
2009
IEEE
13 years 5 months 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
ICASSP
2011
IEEE
12 years 11 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
14 years 1 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
CASES
2003
ACM
14 years 1 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»
13 years 2 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