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CORR
2010
Springer
210views Education» more  CORR 2010»
14 years 9 months ago
Exploiting Statistical Dependencies in Sparse Representations for Signal Recovery
Signal modeling lies at the core of numerous signal and image processing applications. A recent approach that has drawn considerable attention is sparse representation modeling, in...
Tomer Faktor, Yonina C. Eldar, Michael Elad
NECO
2010
154views more  NECO 2010»
14 years 8 months ago
Role of Homeostasis in Learning Sparse Representations
Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the emergence of this response is to state that ...
Laurent U. Perrinet
IPPS
2008
IEEE
15 years 4 months ago
On the representation and multiplication of hypersparse matrices
Multicore processors are marking the beginning of a new era of computing where massive parallelism is available and necessary. Slightly slower but easy to parallelize kernels are ...
Aydin Buluç, John R. Gilbert
ICASSP
2011
IEEE
14 years 1 months ago
Dictionary learning of convolved signals
Assuming that a set of source signals is sparsely representable in a given dictionary, we show how their sparse recovery fails whenever we can only measure a convolved observation...
Daniele Barchiesi, Mark D. Plumbley
ICIP
2009
IEEE
15 years 10 months ago
Reconstructing Ft-ir Spectroscopic Imaging Data With A Sparse Prior
Fourier Transform Infrared (FT-IR) spectroscopic imaging is a potentially valuable tool for diagnosing breast and prostate cancer, but its clinical deployment is limited due to lo...