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TIT
2002
86views more  TIT 2002»
15 years 4 months ago
Lagrangian empirical design of variable-rate vector quantizers: consistency and convergence rates
Abstract--The Lagrangian formulation of variable-rate vector quantization is known to yield useful necessary conditions for quantizer optimality and generalized Lloyd algorithms fo...
Tamás Linder
ICIP
2006
IEEE
16 years 6 months ago
Determining Achievable Rates for Secure, Zero Divergence, Steganography
In steganography (the hiding of data into innocuous covers for secret communication) it is difficult to estimate how much data can be hidden while still remaining undetectable. To...
Kenneth Sullivan, Kaushal Solanki, B. S. Manjunath...
JMLR
2010
195views more  JMLR 2010»
15 years 3 months ago
Online Learning for Matrix Factorization and Sparse Coding
Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statisti...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
PAMI
2012
13 years 7 months ago
Task-Driven Dictionary Learning
—Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience, and signal proce...
Julien Mairal, Francis Bach, Jean Ponce
CVPR
2005
IEEE
16 years 6 months ago
A Bayesian Approach to Unsupervised Feature Selection and Density Estimation Using Expectation Propagation
We propose an approximate Bayesian approach for unsupervised feature selection and density estimation, where the importance of the features for clustering is used as the measure f...
Shaorong Chang, Nilanjan Dasgupta, Lawrence Carin
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