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TASLP
2008
124views more  TASLP 2008»
13 years 7 months ago
Sparse Linear Regression With Structured Priors and Application to Denoising of Musical Audio
Abstract--We describe in this paper an audio denoising technique based on sparse linear regression with structured priors. The noisy signal is decomposed as a linear combination of...
Cédric Févotte, Bruno Torrésa...
NIPS
2003
13 years 9 months ago
Sparse Representation and Its Applications in Blind Source Separation
In this paper, sparse representation (factorization) of a data matrix is first discussed. An overcomplete basis matrix is estimated by using the K−means method. We have proved ...
Yuanqing Li, Andrzej Cichocki, Shun-ichi Amari, Se...
ICA
2007
Springer
13 years 11 months ago
Estimating the Mixing Matrix in Sparse Component Analysis Based on Converting a Multiple Dominant to a Single Dominant Problem
We propose a new method for estimating the mixing matrix, A, in the linear model x(t) = As(t), t = 1, . . . , T, for the problem of underdetermined Sparse Component Analysis (SCA)....
Nima Noorshams, Massoud Babaie-Zadeh, Christian Ju...
CVPR
2003
IEEE
14 years 9 months ago
Practical Non-parametric Density Estimation on a Transformation Group for Vision
It is now common practice in machine vision to define the variability in an object's appearance in a factored manner, as a combination of shape and texture transformations. I...
Erik G. Miller, Christophe Chefd'Hotel
TSP
2010
13 years 2 months ago
Efficient recursive estimators for a linear, time-varying Gaussian model with general constraints
The adaptive estimation of a time-varying parameter vector in a linear Gaussian model is considered where we a priori know that the parameter vector belongs to a known arbitrary s...
Stefan Uhlich, Bin Yang