Publication

Online dictionary learning over distributed models

Publications associées (34)

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

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

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

Greedy Dictionary Selection for Sparse Representation

Volkan Cevher, Andreas Krause

We develop an efficient learning framework to construct signal dictionaries for sparse representation by selecting the dictionary columns from multiple candidate bases. By sparse, we mean that only a few dictionary elements, compared to the ambient signal ...

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

A union of incoherent spaces model for classification

Pierre Vandergheynst, Karin Schnass

We present a new and computationally efficient scheme for classifying signals into a fixed number of known classes. We model classes as subspaces in which the corresponding data is well represented by a dictionary of features. In order to ensure low miscla ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2010

Ultrasound Tomography with Learned Dictionaries

Martin Vetterli, Pascal Frossard, Ivana Tosic, Ivana Jovanovic

We propose a new method for imaging sound speed in breast tissue from measurements obtained by ultrasound tomography (UST) scan- ners. Given the measurements, our algorithm finds a sparse image representation in an overcomplete dictionary that is adapted t ...

2010

Greedy dictionary selection for sparse representation

Volkan Cevher

2009

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