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CORR
2010
Springer
136views Education» more  CORR 2010»
13 years 11 months ago
Optimally Sparse Frames
Frames have established themselves as a means to derive redundant, yet stable decompositions of a signal for analysis or transmission, while also promoting sparse expansions. Howe...
Peter G. Casazza, Andreas Heinecke, Felix Krahmer,...
CORR
2010
Springer
143views Education» more  CORR 2010»
13 years 8 months ago
CUR from a Sparse Optimization Viewpoint
The CUR decomposition provides an approximation of a matrix X that has low reconstruction error and that is sparse in the sense that the resulting approximation lies in the span o...
Jacob Bien, Ya Xu, Michael W. Mahoney
ICASSP
2011
IEEE
13 years 2 months ago
Soft frame margin estimation of Gaussian Mixture Models for speaker recognition with sparse training data
—Discriminative Training (DT) methods for acoustic modeling, such as MMI, MCE, and SVM, have been proved effective in speaker recognition. In this paper we propose a DT method fo...
Yan Yin, Qi Li
ICASSP
2011
IEEE
13 years 2 months ago
Sparse video recovery using Linearly Constrained Gradient Projection
This paper concerns the reconstruction of a temporally-varying scene from a video sequence of noisy linear projections. Assuming that each video frame is sparse or compressible in...
Daniel Thompson, Zachary T. Harmany, Roummel F. Ma...
ICASSP
2011
IEEE
13 years 2 months ago
Additive character sequences with small alphabets for compressed sensing matrices
Compressed sensing is a novel technique where one can recover sparse signals from the undersampled measurements. In this paper, a K × N measurement matrix for compressed sensing ...
Nam Yul Yu