Farouk Yahaya

Farouk Yahaya
Université de Bordeaux · Centre de Recherche Inria Bordeaux - Sud-Ouest

Ph.D.
Postdoctoral Research Associate

About

16
Publications
12,075
Reads
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46
Citations
Introduction
Research Interests - sensor network data processing - nonnegative matrix factorization - environmental monitoring website: www.fyahaya.com
Additional affiliations
September 2021 - December 2021
University of the Littoral Opal Coast
Position
  • Professor (Assistant)
November 2017 - present
University of the Littoral Opal Coast
Position
  • PhD Student
Description
  • PhD research currently being conducted at the Laboratoire d’Informatique Signal et Image de la Côte d’Opale (LISIC)
Education
November 2017 - June 2020
University of the Littoral Opal Coast
Field of study
  • Computer Science

Publications

Publications (16)
Article
Full-text available
This paper presents a novel and unreported approach developed to filter T2-weighetd Magnetic Resonance Imaging (MRI). The MRI data is fitted with a parametric bivariate cubic Lagrange polynomial, which is used as the model function to build the continuum into the discrete samples of the two-dimensional MRI images. On the basis of the aforementioned...
Article
Full-text available
This paper proposes a novel edge detection algorithm based on model fitting and the calculation of the first order derivative of two-dimensional images. It is central to mention that the aforementioned calculation can only be done through approximations since the image being used is made of a sequel of discrete samples, and the continuity is brough...
Poster
Full-text available
Modern latent variable analysis methods—e.g., sparse approximation, robust principal component analysis, dictionary learning—have been massively investigated for more than two decades and were successfully applied to signal, image, or video processing, and to machine learning. Among these techniques, Nonnegative Matrix Factorization (NMF) attracted...
Conference Paper
Full-text available
Random projections have been recently implemented in Nonnegative Matrix Factorization (NMF) to speed-up the NMF computations, with a negligible loss of performance. In this paper, we investigate the effects of such projections when the NMF technique uses the fast Nesterov gradient descent (NeNMF). We experimentally show the randomized subspace iter...
Article
In this work, we introduce a novel image zooming methodology that transitions from a nonadaptive Sin‐based approach to an adaptive Sinc‐based zooming technique. The two techniques base their theoretical foundation on the Whittaker–Shannon interpolation formula and the Nyquist theorem. The evolution into adaptive Sinc‐based zoom is accomplished thro...
Article
In this paper we propose a novel framework that successfully combines random projection or compression to weighted Nonnegative Matrix Factorization (NMF). Indeed a large body of NMF research has focused on the unweighted case—i.e., a complete data matrix to factorize—with a few extensions to handle incomplete data. Also most of these works are typi...
Thesis
Full-text available
Air pollution poses substantial health issues with several hundred thousands of premature deaths in Europe each year. Effective air quality monitoring is thus an major task for environmental agencies. It is usually carried out by some highly accurate monitoring stations. However, these stations are expensive and limited in number, thus providing a...
Conference Paper
Random projections recently became popular tools to process big data. When applied to Nonnegative Matrix Factorization (NMF), it was shown that, in practice, with the same compression level, structured random projections were more efficient than classical strategies based on, e.g., Gaussian compression. However, as they are data-dependent, they rem...
Conference Paper
Full-text available
In this paper, we assume a set of mobile geolocalized sensor arrays observing an area over time. Each of these arrays consists of heterogeneous and cross-sensitive sensors, i.e., the sensor readings provided by one of such sensors also depends on the readings of the other sensors in the array. We further assume that such arrays are possibly-uncalib...
Preprint
Full-text available
Random projections became popular tools to process big data. In particular, when applied to Nonnegative Matrix Factorization (NMF), it was shown that structured random projections were far more efficient than classical strategies based on gaussian compression. However, they remain costly and might not fully benefit from recent fast random projectio...
Conference Paper
Full-text available
Random projections belong to the major techniques to process big data and have been successfully applied to Nonnegative Matrix Factorization (NMF). However, they cannot be applied in the case of missing entries in the matrix to factorize, which occurs in many actual problems with large data matrices. In this paper, we thus aim to solve this issue a...
Preprint
Full-text available
Random projections have been recently implemented in Nonnegative Matrix Factorization (NMF) to speed-up the NMF computations, with a negligible loss of performance. In this paper, we investigate the effects of such projections when the NMF technique uses the fast Nesterov gradient descent (NeNMF). We experimentally show the randomized subspace iter...

Questions

Questions (9)

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