This is the accepted version of an article published in Lecture Notes in Computer Science Volume 7191, 2012, pp 231-238. The final publication is available at link.springer.com http://www.springerlink.com/content/l1k4514765283618
The idea that many important classes of signals can be well-represented by linear combi-nations of a...
Dictionary Learning (DL) has seen widespread use in signal processing and machine learning. Given a ...
Assuming that a set of source signals is sparsely representable in a given dictionary, we show how t...
We develop an efficient learning framework to construct signal dictionaries for sparse representatio...
© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
This work presents a geometrical analysis of the Large Step Gradient Descent (LGD) dictionary learni...
In this paper, we consider the dictionary learning problem for the sparse analysis model. A novel al...
For dictionary-based decompositions of certain types, it has been observed that there might be a lin...
A powerful approach to sparse representation, dictionary learning consists in finding a redundant fr...
During the past decade, sparse representation has attracted much attention in the signal processing ...
International audienceThis article treats the problem of learning a dictionary providing sparse repr...
International audienceWe propose an ℓ1 criterion for dictionary learning for sparse signal represent...
We consider the problem of sparse coding, where each sample consists of a sparse linear combination ...
In order to find sparse approximations of signals, an appropriate generative model for the signal cl...
This is an abstract of the full preprint available at http://hal.inria.fr/hal-00918142/National audi...
The idea that many important classes of signals can be well-represented by linear combi-nations of a...
Dictionary Learning (DL) has seen widespread use in signal processing and machine learning. Given a ...
Assuming that a set of source signals is sparsely representable in a given dictionary, we show how t...
We develop an efficient learning framework to construct signal dictionaries for sparse representatio...
© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
This work presents a geometrical analysis of the Large Step Gradient Descent (LGD) dictionary learni...
In this paper, we consider the dictionary learning problem for the sparse analysis model. A novel al...
For dictionary-based decompositions of certain types, it has been observed that there might be a lin...
A powerful approach to sparse representation, dictionary learning consists in finding a redundant fr...
During the past decade, sparse representation has attracted much attention in the signal processing ...
International audienceThis article treats the problem of learning a dictionary providing sparse repr...
International audienceWe propose an ℓ1 criterion for dictionary learning for sparse signal represent...
We consider the problem of sparse coding, where each sample consists of a sparse linear combination ...
In order to find sparse approximations of signals, an appropriate generative model for the signal cl...
This is an abstract of the full preprint available at http://hal.inria.fr/hal-00918142/National audi...
The idea that many important classes of signals can be well-represented by linear combi-nations of a...
Dictionary Learning (DL) has seen widespread use in signal processing and machine learning. Given a ...
Assuming that a set of source signals is sparsely representable in a given dictionary, we show how t...