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Dictionary Learning for Two-Dimensional Kendall Shapes

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Landmark-Based Shape Encoding and Sparse-Dictionary Learning in the Continuous Domain

Michaël Unser, Daniel Andreas Schmitter

We provide a generic framework to learn shape dictionaries of landmark-based curves that are defined in the continuous domain. We first present an unbiased alignment method that involves the construction of a mean shape as well as training sets whose eleme ...

2018

Multi-task additive models with shared transfer functions based on dictionary learning

Pascal Frossard, Alhussein Fawzi

Additive models form a widely popular class of regression models which represent the relation between covariates and response variables as the sum of low-dimensional transfer functions. Besides flexibility and accuracy, a key benefit of these models is the ...

Institute of Electrical and Electronics Engineers2017

Dictionary Learning Based on Sparse Distribution Tomography

Michaël Unser, Pedram Pad

We propose a new statistical dictionary learning algorithm for sparse signals that is based on an α-stable innovation model. The parameters of the underlying model—that is, the atoms of the dictionary, the sparsity index α and the dispersion of the transfo ...

P. Pad2017

Learning from sparse codes

Pascal Frossard, Sofia Karygianni

In this paper we address the problem of learning image structures directly from sparse codes. We first model images as linear combinations of molecules, which are themselves groups of atoms from a redundant dictionary. We then formulate a new structure lea ...

Ieee2016

Signal structure: from manifolds to molecules and structured sparsity

Sofia Karygianni

Effective representation methods and proper signal priors are crucial in most signal processing applications. In this thesis we focus on different structured models and we design appropriate schemes that allow the discovery of low dimensional latent struct ...

EPFL2015

Dictionary learning for fast classification based on soft-thresholding

Pascal Frossard, Alhussein Fawzi

Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits t ...

Springer2015

Multi-Graph Learning of Spectral Graph Dictionaries

Pascal Frossard

We study the problem of learning constitutive features for the effective representation of graph signals, which can be considered as observations collected on different graph topologies. We propose to learn graph atoms and build graph dictionaries that pro ...

2015

Dictionary Learning Over Distributed Models

Ali H. Sayed

In this paper, we consider learning dictionary models over a network of agents, where each agent is only in charge of a portion of the dictionary elements. This formulation is relevant in Big Data scenarios where large dictionary models may be spread over ...

IEEE2015

Transformation-Invariant Dictionary Learning for Classification with 1-Sparse Representations

Pascal Frossard, Elif Vural, Ahmet Caner Yüzügüler

Sparse representations of images in well-designed dictionaries can be used for effective classification. Meanwhile, training data available in most realistic settings are likely to be exposed to geometric transformations, which poses a challenge for the de ...

2014

Compressed Sensing of Memoryless Sources

Saeid Haghighatshoar

Compressed sensing is a new trend in signal processing for efficient sampling and signal acquisition. The idea is that most real-world signals have a sparse representation in an appropriate basis and this can be exploited to capture the sparse signal by ta ...

EPFL2014