Publications related to Dictionary Learning for Two-Dimensional Kendall Shapes

Online dictionary learning over distributed models

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 multiple large dictionary models may be spr ...

IEEE2014

Fast detection of novel problematic patterns based on dictionary learning and prediction of their lithographic difficulty

Andreas Krause, Frank Grégoire Jean de Morsier, Maria Gabrani

Assessing pattern printability in new large layouts faces important challenges of runtime and false detection. Lithographic simulation tools and classification techniques do not scale well. We propose a fast pattern detection method by learning an overcomp ...

2014

Classification of unions of subspaces with sparse representations

Pascal Frossard, Alhussein Fawzi

We propose a preliminary investigation on the benefits and limitations of classifiers based on sparse representations. We specifically focus on the union of subspaces data model and examine binary classifiers built on a sparse non linear mapping (in a redu ...

2013

Convexity in source separation: Models, geometry, and algorithms

Volkan Cevher, Afsaneh Asaei, Quoc Tran Dinh, Luca Baldassarre

Source separation, or demixing, is the process of extracting multiple components entangled within a signal. Contemporary signal processing presents a host of difficult source separation problems, from interference cancellation to background subtraction, bl ...

2013

Sparse Approximation Using M-Term Pursuit and Application in Image and Video Coding

Pascal Frossard, Pierre Vandergheynst, Adel Rahmoune

This paper introduces a novel algorithm for sparse approximation in redundant dictionaries called the M-term pursuit (MTP). This algorithm decomposes a signal into a linear combination of atoms that are selected in order to represent the main signal compon ...

Institute of Electrical and Electronics Engineers2012

Learning of Structured Graph Dictionaries

Pascal Frossard, Xiaowen Dong, Xue Zhang

We propose a method for learning dictionaries towards sparse approximation of signals defined on vertices of arbitrary graphs. Dictionaries are expected to describe effectively the main spatial and spectral components of the signals of interest, so that th ...

2012

Dictionary learning in stereo imaging

Pascal Frossard, Ivana Tosic

This paper presents a new method for learning overcomplete dictionaries adapted to efficient joint representation of stereo images. We first formulate a sparse stereo image model where the multi-view correlation is described by local geometric transforms o ...

Institute of Electrical and Electronics Engineers2011

Deliverable 3.2: Report on Discovering Structure within Dictionary Learning

In this work package (WP), we investigate the possibility of discovering structure within dictionary learning. This could range from exploring groups of atoms that appear in clusters - a form of molecule learning - to learning graphical dependencies across ...

2011

Learning dictionaries of spatial and temporal EEG primitives for brain-computer interfaces

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Benjamin Hamner

Sparse methods are widely used in image and audio processing for denoising and classification, but there have been few previous applications to neural signals for brain-computer interfaces (BCIs). We used the dictionary- learning algorithm K-SVD, coupled w ...

2011

Dictionary Identification - Sparse Matrix-Factorisation via $\ell_1$-Minimisation

Rémi Gribonval, Karin Schnass

This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via

\ell_1

-minimisation. The problem can also be seen as factorising a

\ddim \times \nsig

matrix $Y=(y_1 \ldots y_\nsig), , y_n\in \R^\ ...

Institute of Electrical and Electronics Engineers2010

Dictionary Learning for Two-Dimensional Kendall Shapes

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Online dictionary learning over distributed models

Fast detection of novel problematic patterns based on dictionary learning and prediction of their lithographic difficulty

Classification of unions of subspaces with sparse representations

Convexity in source separation: Models, geometry, and algorithms

Sparse Approximation Using M-Term Pursuit and Application in Image and Video Coding

Learning of Structured Graph Dictionaries

Dictionary learning in stereo imaging

Deliverable 3.2: Report on Discovering Structure within Dictionary Learning

Learning dictionaries of spatial and temporal EEG primitives for brain-computer interfaces

Dictionary Identification - Sparse Matrix-Factorisation via $\ell_1$-Minimisation

Online dictionary learning over distributed models

Fast detection of novel problematic patterns based on dictionary learning and prediction of their lithographic difficulty

Convexity in source separation: Models, geometry, and algorithms

Deliverable 3.2: Report on Discovering Structure within Dictionary Learning

Learning dictionaries of spatial and temporal EEG primitives for brain-computer interfaces

Sparse Approximation Using M-Term Pursuit and Application in Image and Video Coding

Learning of Structured Graph Dictionaries

Dictionary learning in stereo imaging

Classification of unions of subspaces with sparse representations

Dictionary Identification - Sparse Matrix-Factorisation via $\ell_1$-Minimisation