David Brie

Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithms

Coupled tensor decompositions (CTDs) perform data fusion by linking factors from different datasets. Although many CTDs have been already proposed, current works do not address important...

Tensor-based framework for training flexible neural networks

Activation functions (AFs) are an important part of the design of neural networks (NNs), and their choice plays a predominant role in the performance of a NN. In this work, we are particularly...

Transient performance analysis of zero-attracting LMS

Zero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for online sparse system identification. It combines the LMS framework and $\ell_1$-norm regularization to promote...

A Simultaneous Sparse Approximation Method for Multidimensional Harmonic Retrieval

In this paper, a sparse-based method for the estimation of the parameters of multidimensional ($R$-D) modal (harmonic or damped) complex signals in noise is presented. The problem is formulated as...

Analysis of Hyperspectral Images Using Supervised Learning Techniques

Modeling of force-volume images in atomic force microscopy

Rigid Registration of Monomodal and Multimodal Images for Wood Pieces Analysis

Fast deconvolution of large fluorescence hyperspectral images

On the modeling and processing of polarization in Raman spectroscopy

Impact of sparse representation on the admissible solutions of spectral unmixing by non-negative matrix factorization

Coupled Tensor Factorization for Flow Cytometry Data Analysis

A non-negative multilinear block tensor decomposition approach to flow cytometry data analysis

A regularized sparse approximation method for hyperspectral image classification

A sparse approach for DOA estimation with a multiple spatial invariance sensor array

Characterization of Finite Signals with Low-Rank Stft

On-Line Blind Unmixing For Hyperspectral Pushbroom Imaging Systems

All-Automatic 3D BIM Modeling of Existing Buildings

Some Rank Conditions for the Identifiability of the Sparse Paralind Model