Approximate joint diagonalization within the Riemannian geometry framework. - Florent Bouchard; Louis Korczowski; Jérôme Malick; Marco Congedo (EUSIPCO, 2016)

Mining the bilinear structure of data with approximate joint diagonalization. - Louis Korczowski; Florent Bouchard; Christian Jutten; Marco Congedo (EUSIPCO, 2016)

A closed-form unsupervised geometry-aware dimensionality reduction method in the Riemannian Manifold of SPD matrices. - Marco Congedo; Pedro Luiz Coelho Rodrigues; Florent Bouchard; Alexandre Barachant; Christian Jutten (EMBC, 2017)

Dimensionality Reduction for BCI Classification using Riemannian Geometry. - Pedro Luiz Coelho Rodrigues; Florent Bouchard; Marco Congedo; Christian Jutten (GBCIC, 2017)

Approximate Joint Diagonalization According to the Natural Riemannian Distance. - Florent Bouchard; Jérôme Malick; Marco Congedo (LVA/ICA, 2017)

Riemannian Optimization and Approximate Joint Diagonalization for Blind Source Separation. - Florent Bouchard; Jérôme Malick; Marco Congedo (IEEE Trans. Signal Process., 2018)

Géométrie et optimisation riemannienne pour la diagonalisation conjointe : application à la séparation de sources d'électroencéphalogrammes. (Riemannian geometry and optimization for approximate joint diagonalization : application to source separation of electroencephalograms). - Florent Bouchard (2018)

Random Matrix Improved Covariance Estimation for a Large Class of Metrics. - Malik Tiomoko; Romain Couillet; Florent Bouchard; Guillaume Ginolhac (ICML, 2019)

Intrinsic Cramér-Rao Bounds for Scatter and Shape Matrices Estimation in CES Distributions. - Arnaud Breloy; Guillaume Ginolhac; Alexandre Renaux; Florent Bouchard (IEEE Signal Process. Lett., 2019)

A Riemannian approach to blind separation of t-distributed sources. - Florent Bouchard; Arnaud Breloy; Guillaume Ginolhac; Alexandre Renaux (EUSIPCO, 2020)

Riemannian Framework for Robust Covariance Matrix Estimation in Spiked Models. - Florent Bouchard; Arnaud Breloy; Guillaume Ginolhac; Frédéric Pascal 001 (ICASSP, 2020)

Riemannian Geometry and Cramér-rao Bound for Blind Separation of Gaussian Sources. - Florent Bouchard; Arnaud Breloy; Alexandre Renaux; Guillaume Ginolhac (ICASSP, 2020)

Approximate Joint Diagonalization with Riemannian Optimization on the General Linear Group. - Florent Bouchard; Bijan Afsari; Jérôme Malick; Marco Congedo (SIAM J. Matrix Anal. Appl., 2020)

Riemannian geometry for compound Gaussian distributions: Application to recursive change detection. - Florent Bouchard; Ammar Mian; Jialun Zhou; Salem Said; Guillaume Ginolhac; Yannick Berthoumieu (Signal Process., 2020)

On-line Kronecker Product Structured Covariance Estimation with Riemannian geometry for t-distributed data. - Florent Bouchard; Arnaud Breloy; Ammar Mian; Guillaume Ginolhac (EUSIPCO, 2021)

A Tyler-Type Estimator of Location and Scatter Leveraging Riemannian Optimization. - Antoine Collas; Florent Bouchard; Arnaud Breloy; Chenfang Ren; Guillaume Ginolhac; Jean Philippe Ovarlez (ICASSP, 2021)

A Riemannian Framework for Low-Rank Structured Elliptical Models. - Florent Bouchard; Arnaud Breloy; Guillaume Ginolhac; Alexandre Renaux; Frédéric Pascal 001 (IEEE Trans. Signal Process., 2021)

Probabilistic PCA From Heteroscedastic Signals: Geometric Framework and Application to Clustering. - Antoine Collas; Florent Bouchard; Arnaud Breloy; Guillaume Ginolhac; Chengfang Ren; Jean Philippe Ovarlez (IEEE Trans. Signal Process., 2021)

Riemannian Classification of EEG Signals with Missing Values. - Alexandre Hippert-Ferrer; Ammar Mian; Florent Bouchard; Frédéric Pascal 001 (EUSIPCO, 2022)

On the Use of Geodesic Triangles between Gaussian Distributions for Classification Problems. - Antoine Collas; Florent Bouchard; Guillaume Ginolhac; Arnaud Breloy; Chengfang Ren; Jean Philippe Ovarlez (ICASSP, 2022)

t-WDA: A novel Discriminant Analysis applied to EEG classification. - Imen Ayadi; Florent Bouchard; Frédéric Pascal 001 (EUSIPCO, 2023)

Elliptical Wishart Distribution: Maximum Likelihood Estimator from Information Geometry. - Imen Ayadi; Florent Bouchard; Frédéric Pascal 001 (ICASSP, 2023)

Learning Graphical Factor Models with Riemannian Optimization. - Alexandre Hippert-Ferrer; Florent Bouchard; Ammar Mian; Titouan Vayer; Arnaud Breloy (ECML/PKDD (4), 2023)

The Fisher-Rao geometry of CES distributions. - Florent Bouchard; Arnaud Breloy; Antoine Collas; Alexandre Renaux; Guillaume Ginolhac (CoRR, 2023)

Natural Bayesian Cramér-Rao Bound with an Application to Covariance Estimation. - Florent Bouchard; Alexandre Renaux; Guillaume Ginolhac; Arnaud Breloy (CoRR, 2023)

Leveraging Standardization in Graph Learning. - Thu Ha Phi; Alexandre Hippert-Ferrer; Florent Bouchard; Arnaud Breloy (EUSIPCO, 2024)

Robust Low-Rank Correlation Fitting. - Thu Ha Phi; Alexandre Hippert-Ferrer; Florent Bouchard; Arnaud Breloy (ICASSP, 2024)

Random matrix theory improved Fréchet mean of symmetric positive definite matrices. - Florent Bouchard; Ammar Mian; Malik Tiomoko; Guillaume Ginolhac; Frédéric Pascal 001 (ICML, 2024)

Online change detection in SAR time-series with Kronecker product structured scaled Gaussian models. - Ammar Mian; Guillaume Ginolhac; Florent Bouchard; Arnaud Breloy (Signal Process., 2024)

Intrinsic Bayesian Cramér-Rao Bound With an Application to Covariance Matrix Estimation. - Florent Bouchard; Alexandre Renaux; Guillaume Ginolhac; Arnaud Breloy (IEEE Trans. Inf. Theory, 2024)

Elliptical Wishart distributions: Information geometry, maximum likelihood estimator, performance analysis and statistical learning. - Imen Ayadi; Florent Bouchard; Frédéric Pascal 001 (Signal Process., 2025)

Beyond $R$-Barycenters: An Effective Averaging Method on Stiefel and Grassmann Manifolds. - Florent Bouchard; Nils Laurent; Salem Said; Nicolas Le Bihan (IEEE Signal Process. Lett., 2025)

Leveraging Low-rank Factorizations of Conditional Correlation Matrices in Graph Learning. - Thu Ha Phi; Alexandre Hippert-Ferrer; Florent Bouchard; Arnaud Breloy (CoRR, 2025)