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- Boris Mailhé, Daniele Barchiesi, Mark D. Plumbley
- 2012 IEEE International Conference on Acoustics…
- 2012

This work considers the problem of learning an incoherent dictionary that is both adapted to a set of training data and incoherent so that existing sparse approximation algorithms can recover the sparsest representation. A new decorrelation method is presented that computes a fixed coherence dictionary close to a given dictionary. That step iterates… (More)

- Boris Mailhé, Sylvain Lesage, Rémi Gribonval, Frédéric Bimbot, Pierre Vandergheynst
- 2008 16th European Signal Processing Conference
- 2008

Shift-invariant dictionaries are generated by taking all the possible shifts of a few short patterns. They are helpful to represent long signals where the same pattern can appear several times at different positions. We present an algorithm that learns shift invariant dictionaries from long training signals. This algorithm is an extension of K-SVD. It… (More)

- Gianluca Monaci, Philippe Jost, Pierre Vandergheynst, Boris Mailhé, Sylvain Lesage, Rémi Gribonval
- IEEE Transactions on Image Processing
- 2007

Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving… (More)

- Boris Mailhé, Rémi Gribonval, Frédéric Bimbot, Pierre Vandergheynst
- 2009 IEEE International Conference on Acoustics…
- 2009

We propose a variant of Orthogonal Matching Pursuit (OMP), called LoCOMP, for scalable sparse signal approximation. The algorithm is designed for shift-invariant signal dictionaries with localized atoms, such as time-frequency dictionaries, and achieves approximation performance comparable to OMP at a computational cost similar to Matching Pursuit.… (More)

- Rémi Gribonval, Boris Mailhé, Holger Rauhut, Karin Schnass, Pierre Vandergheynst
- 2007 IEEE International Conference on Acoustics…
- 2007

This paper introduces p-thresholding, an algorithm to compute simultaneous sparse approximations of multichannel signals over redundant dictionaries. We work out both worst case and average case recovery analyses of this algorithm and show that the latter results in much weaker conditions on the dictionary. Numerical simulations confirm our theoretical… (More)

- Boris Mailhé, Rémi Gribonval, Pierre Vandergheynst, Frédéric Bimbot
- Signal Processing
- 2011

In this work we present a new greedy algorithm for sparse approximation called LocOMP. LocOMP is meant to be run on local dictionaries made of atoms with much shorter supports than the signal length. This notably encompasses shift-invariant dictionaries and time-frequency dictionaries, be they monoscale or multiscale. In this case, very fast implementations… (More)

- Boris Mailhé, Rémi Gribonval, Frédéric Bimbot, Mathieu Lemay, Pierre Vandergheynst, Jean-Marc Vesin
- 2009 IEEE International Conference on Acoustics…
- 2009

We propose a new method for ventricular cancellation and atrial modelling in the ECG of patients suffering from atrial fibrillation. Our method is based on dictionary learning. It extends both the average beat subtraction and the sparse source separation approaches. Experiments on synthetic data show that this method can almost completely suppress the… (More)

- Boris Mailhé, Mark D. Plumbley
- LVA/ICA
- 2012

This work presents a new algorithm for dictionary learning. Existing algorithms such as MOD and K-SVD often fail to find the best dictionary because they get trapped in a local minimum. Olshausen and Field's Sparsenet algorithm relies on a fixed step projected gradient descent. With the right step, it can avoid local minima and converge towards the global… (More)

This paper presents a methodology for extracting meaningful synchronous structures from multi-modal signals. Simultaneous processing of multi-modal data can reveal information that is unavailable when handling the sources separately. However, in natural high-dimensional data, the statistical dependencies between modalities are, most of the time, not… (More)

- Boris Mailhé, Henri Prade
- KR
- 2004

In this paper, partial knowledge about the possible transitions which can take place in a dynamical environment is represented by a set of pairs of propositional formulae, with the following intended meaning: If the first one is true then the second will be true at the next step. More generally, a certainty level representing the lower bound of a necessity… (More)