Search Results for author: Christian Jutten

Found 8 papers, 1 papers with code

Electrode Selection for Noninvasive Fetal Electrocardiogram Extraction using Mutual Information Criteria

no code implementations1 Feb 2023 Reza Sameni, Frédéric Vrins, Fabienne Parmentier, Christophe Hérail, Vincent Vigneron, Michel Verleysen, Christian Jutten, Mohammad B. Shamsollahi

Based on this idea, in previous works array recording systems and sensor selection strategies based on the Mutual Information (MI) criterion have been developed.

A graph representation based on fluid diffusion model for data analysis: theoretical aspects and enhanced community detection

no code implementations7 Dec 2021 Andrea Marinoni, Christian Jutten, Mark Girolami

This system provides several constraints and assumptions on the data properties that might be not valid for multimodal data analysis, especially when large scale datasets collected from heterogeneous sources are considered, so that the accuracy and robustness of the outcomes might be severely jeopardized.

Community Detection valid

Temporally Nonstationary Component Analysis; Application to Noninvasive Fetal Electrocardiogram Extraction

no code implementations20 Aug 2021 Fahimeh Jamshidian-Tehrani, Reza Sameni, Christian Jutten

The nonstationarity of the source signals can be used as a discriminative property for signal separation.

A Hypothesis Testing Approach to Nonstationary Source Separation

no code implementations14 May 2021 Reza Sameni, Christian Jutten

The extraction of nonstationary signals from blind and semi-blind multivariate observations is a recurrent problem.


Enhancing ensemble learning and transfer learning in multimodal data analysis by adaptive dimensionality reduction

no code implementations8 May 2021 Andrea Marinoni, Saloua Chlaily, Eduard Khachatrian, Torbjørn Eltoft, Sivasakthy Selvakumaran, Mark Girolami, Christian Jutten

Nonetheless, when applied to multimodal datasets (i. e., datasets acquired by means of multiple sensing techniques or strategies), the state-of-theart methods for ensemble learning and transfer learning might show some limitations.

Dimensionality Reduction Ensemble Learning +1

Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review

1 code implementation21 Jan 2020 Ricardo Augusto Borsoi, Tales Imbiriba, José Carlos Moreira Bermudez, Cédric Richard, Jocelyn Chanussot, Lucas. Drumetz, Jean-Yves Tourneret, Alina Zare, Christian Jutten

The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an image.

On the Achievability of Cramér-Rao Bound In Noisy Compressed Sensing

no code implementations13 Jun 2010 Rad Niazadeh, Masoud Babaie-Zadeh, Christian Jutten

Recently, it has been proved in Babadi et al. that in noisy compressed sensing, a joint typical estimator can asymptotically achieve the Cramer-Rao lower bound of the problem. To prove this result, this paper used a lemma, which is provided in Akcakaya et al, that comprises the main building block of the proof.


Cannot find the paper you are looking for? You can Submit a new open access paper.