Publications associées à Semantic coding by supervised dimensionality reduction

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A Scalable Formulation of Probabilistic Linear Discriminant Analysis: Applied to Face Recognition

Sébastien Marcel, Laurent El Shafey

In this paper we present a scalable and exact solution for probabilistic linear discriminant analysis (PLDA). PLDA is a probabilistic model that has been shown to provide state-of-the-art performance for both face and speaker recognition. However, it has o ...

Idiap2013

Low-rank Matrix Approximation Using Point-wise Operators

Arash Amini, Amin Karbasi

Extracting low dimensional structure from high dimensional data arises in many applications such as machine learning, statistical pattern recognition, wireless sensor networks, and data compression. If the data is restricted to a lower dimensional subspace ...

Institute of Electrical and Electronics Engineers2012

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Pierre Vandergheynst, Mohammad Golbabaee

In this paper we consider recovery of a high dimensional data matrix from a set of incomplete and noisy linear measurements. We introduce a new model which can efficiently restricts the degrees of freedom of data and, at the same time, is generic so that f ...

2012

Subspaces Indexing Model on Grassmann Manifold for Image Search

Xinchao Wang

Conventional linear subspace learning methods like principal component analysis (PCA), linear discriminant analysis (LDA) derive subspaces from the whole data set. These approaches have limitations in the sense that they are linear while the data distribut ...

Institute of Electrical and Electronics Engineers2011

Are Sparse Representations Really Relevant for Image Classification?

Vincent Lepetit, Roberto Rigamonti, Matthew Brown

Recent years have seen an increasing interest in sparse representations for image classiﬁcation and object recognition, probably motivated by evidence from the analysis of the primate visual cortex. It is still unclear, however, whether or not sparsity hel ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Learning Low-Dimensional Signal Models

Volkan Cevher

Sampling, coding, and streaming even the most essential data, e.g., in medical imaging and weather-monitoring applications, produce a data deluge that severely stresses the avail able analog-to-digital converter, communication bandwidth, and digital-storag ...

Institute of Electrical and Electronics Engineers2011

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei

We leverage the recent algorithmic advances in compressive sensing, and propose a novel source separation algorithm for efficient recovery of convolutive speech mixtures in spectro-temporal domain. Compared to the common sparse component analysis technique ...

Idiap2011

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei

IEEE Service Center, 445 Hoes Lane, PO Box 1331, Piscataway, NJ 08855-1331 USA2011

Model-based Compressive Sensing for Multi-party Distant Speech Recognition

Hervé Bourlard, Volkan Cevher, Afsaneh Asaei

2011

Semantic coding by supervised dimensionality reduction

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A Scalable Formulation of Probabilistic Linear Discriminant Analysis: Applied to Face Recognition

Low-rank Matrix Approximation Using Point-wise Operators

Image-Based Localization Using the Plenoptic Function

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Subspaces Indexing Model on Grassmann Manifold for Image Search

Are Sparse Representations Really Relevant for Image Classification?

Learning Low-Dimensional Signal Models

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

Model-based Compressive Sensing for Multi-party Distant Speech Recognition

Low-rank Matrix Approximation Using Point-wise Operators

Compressed Sensing of Simultaneous Low-Rank and Joint-Sparse Matrices

Are Sparse Representations Really Relevant for Image Classification?

A Scalable Formulation of Probabilistic Linear Discriminant Analysis: Applied to Face Recognition

Subspaces Indexing Model on Grassmann Manifold for Image Search

Learning Low-Dimensional Signal Models

Image-Based Localization Using the Plenoptic Function

Model-based Compressive Sensing for Multi-party Distant Speech Recognition

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition

Model-Based Compressive Sensing for Multi-Party Distant Speech Recognition