Antoine Collas

(MIND team, Inria Saclay)

Title — Riemannian geometry for statistical estimation and learning: applications to remote sensing and M/EEG data analysis

Abstract — Covariance matrices have emerged as a robust and versatile tool for the statistical analysis of multivariate data, particularly in the domains of M/EEG data analysis. These matrices, being positive definite, can be viewed as points residing on a Riemannian manifold, opening up a new avenue for understanding and harnessing their properties.

In this talk, we begin by introducing a machine learning pipeline, encompassing clustering, classification, and regression, all within the framework of Riemannian geometry. Indeed, we explore several innovative extensions of this pipeline. Firstly, we introduce new divergence measures, based on geodesic triangles between pairs of location-covariance matrices [1]. Additionally, we present a novel, robust estimator for jointly estimating location and covariance matrices [2], which promises to improve robustness to outliers. We also discuss the classification of descriptors following this model. Furthermore, our discussion will conduct us into domain adaptation techniques specifically tailored for regression tasks [3], shedding light on how Riemannian geometry can be applied to adapt to different data domains.

To demonstrate the effectiveness of these methods, we apply them to recent datasets from the fields of remote sensing and M/EEG data analysis, showcasing their real-world utility and versatility.

In the conclusion, we discuss open-source ecosystem surrounding Riemannian geometry for the statistical analysis of multivariate data with Geomstats, Pymanopt and pyCovariance.

References
[1] A. Collas, F. Bouchard, G. Ginolhac, A. Breloy, C. Ren, and J.-P. Ovarlez, On the use of geodesic triangles between gaussian distributions for classification problems, in ICASSP, Singapore, 2022.

[2] A. Collas, A. Breloy, C. Ren, G. Ginolhac, and J.-P. Ovarlez, Riemannian optimization for non-centered mixture of scaled Gaussian distributions, IEEE Transactions on Signal Processing, 2023.

[3] A. Mellot, A. Collas, P. L. Rodrigues, D. Engemann, and A. Gramfort, Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modeling, Submitted to Imaging Neuroscience MIT Press, 2023.

Transcript

1. Riemannian geometry for statistical estimation and learning: applications to remote sensing and M/EEG Antoine Collas S3 seminar - L2S, CentraleSupélec
2. Education and Research • 2019-2022: PhD in Signal processing SONDRA laboratory, CentraleSupélec, University of Paris-Saclay Directors: Jean-Philippe Ovarlez, Guillaume Ginolhac • 2022 - Present: Postdoctoral Researcher in Machine Learning Mind team (ex-Parietal) at Inria Saclay Advisors: Alexandre Gramfort, Rémi Flamary 1/32
3. Table of contents 1. Context 2. Riemannian geometry and problematics 3. Estimation and classification of non centered and heteroscedastic data 4. Probabilistic PCA from heteroscedastic signals 5. Aligning M/EEG data to enhance predictive regression modeling 2/32
4. Context
5. Context in remote sensing In recent years, many image time series have been taken from the earth with different technologies: SAR, multi/hyper spectral imaging, ... Objective Segment semantically these data using sensor diversity (spectral bands, polarization...), and spatial and/or temporal informations. Time Sensor diversity Width Height ... Figure 1: Multivariate image time series. Applications Activity monitoring, land cover mapping, crop type mapping, disaster assessment ... 3/32
6. Context in neuroscience Many new datasets are available in neuroscience: EEG, MEG, fMRI, ... Objectives • Classify brain signals into different cognitive states (sleep, wake, anesthesia, seizure, ...). • Regress biomarkers (e.g. age) from brain signals. 0 1 2 3 4 Time (s) F3 F4 C3 C4 P3 P4 O1 O2 F3 F4 C3 C4 P3 P4 O1 O2 Figure 2: Multivariate EEG time series and the sensor locations. Applications Brain-computer interfaces, sleep monitoring, brain aging, ... 4/32
7. Classification and regression pipeline Step 1: data extraction Step 2: feature estimation Step 3: feature classification/regression {xi}n i=1 min θ∈M L(θ, x1, ..., xn) θ1 + θ2 + θ3 + θ4 + θ5 + E.g : one θ characterizes one pixel in remote sensing or one trial in neuroscience ! θ6 + θ8 + θ9 + Example with 2 classes: white and red Figure 3: Classification and regression pipeline. Assumption: x ∼ f (.; θ), a parametric probability density function, θ ∈ M Examples of θ: θ = Σ a covariance matrix, θ = (µ, Σ) a vector and a covariance matrix, θ = ({τi}, U) a scalar and an orthogonal matrix... M can be constrained ! 5/32
8. Step 2: objectives for feature estimation Figure 4: Example of a SAR image (from nasa.gov). Figure 5: Example of a hyperspectral image (from nasa.gov). Objectives: • develop robust estimators, i.e. estimators for non Gaussian or heterogeneous data because of the high resolution of images and the presence of outliers in biosignals, • develop regularized/structured estimators, i.e. estimators that handle the high dimension of hyperspectral images and MEG. 6/32
9. Step 3: objectives for classification and regression M θ1 θ2 • • • γ(t) Figure 6: Divergence δγ : squared length of the curve γ. M • θ1 • θ2 • θ4 • θ3 • θ Figure 7: Center of mass of {θi}M i=1 . Objectives: Develop divergences that • respect the constraints of M, • are related to the chosen statistical distributions, • are robust to distribution shifts between train and test data. Use normalizations on M to fix distribution shifts between train and test sets. 7/32
10. Classification and regression pipeline and Riemannian geometry Random variable: x ∼ f (.; θ), θ ∈ M Step 2: maximum likelihood estimation minimize θ∈M L(θ|{xi}n i=1 ) = − log f ({xi}n i=1 ; θ) Step 3: given δ, center of mass of {θi}M i=1 minimize θ∈M ∑ i δ(θ, θi) Use of Riemannian geometry: • optimization under constraints, • “Fisher information metric” =⇒ a canonical Riemannian manifold for the parameter space M (fast estimators, intrinsic Carmér-Rao bounds...), • δ: squared Riemannian distance. 8/32
11. Riemannian geometry and problematics
12. What is a Riemannian manifold ? M • θ1 Tθ1 M ξ •θ2 α α = dM(θ1, θ2) Curvature induced by: • constraints, e.g. the sphere: x = 1, • Riemannian metric, e.g. on S++ p : ξ, η S++ p Σ = Tr(Σ−1ξΣ−1η). Some geometric tools: • tangent space TθM (vector space): linearization of M at θ ∈ M, • Riemannian metric ., . M θ : inner product on TθM, • geodesic γ: curve on M with zero acceleration, • distance: dM(θ1, θ2) = length of γ. Examples of M: Rp×k, the sphere Sp−1, symmetric positive definite matrices S++ p , orthonormal k−frames Stp,k , low-rank matrices, … Nicolas Boumal. An introduction to optimization on smooth manifolds. Cambridge University Press, 2023 9/32
13. Optimization on a manifold Optimization L : M → R, smooth minimize θ∈M L(θ) M • minimum • θ0 Tθ0 M − gradM L(θ0) • θ1 10/32
14. Optimization on a manifold Optimization L : M → R, smooth minimize θ∈M L(θ) M • minimum • θ0 Tθ0 M − gradM L(θ0) • θ1 10/32
15. Optimization on a manifold Optimization L : M → R, smooth minimize θ∈M L(θ) M • minimum • θ0 Tθ0 M − gradM L(θ0) • θ1 10/32
16. Optimization on a manifold Optimization L : M → R, smooth minimize θ∈M L(θ) M • minimum • θ0 Tθ0 M − gradM L(θ0) • θ1 10/32
17. Fisher information metric Random variable, negative log-likelihood x ∼ f (.; θ), θ ∈ M L (θ|x) = − log f (x; θ) Fisher information metric ξ, η FIM θ = Ex∼f (.;θ) [ D2 L (θ|x) [ξ, η] ] = vec(ξ)TI(θ)vec(η) where I(θ) = Ex∼f (.;θ) [Hess L(θ|x)] ∈ S++ p is the Fisher information matrix. (Set of constraints, Fisher information metric) = a Riemannian manifold 11/32
18. Existing work: centered Gaussian A well known geometry: x ∼ N(0, Σ), Σ ∈ S++ p with the Fisher information metric: ξ, η FIM Σ = Tr Σ−1ξΣ−1η . Induced pipeline Step 2: ˆ ΣSCM = 1 n n i=1 xixT i . Step 3: geodesic distance on S++ p d S++ p (Σ1, Σ2) = log Σ− 1 2 1 Σ2Σ− 1 2 1 2 . Riemannian gradient descent to solve: minimize Σ∈S++ p i d2 S++ p (Σ, Σi). Alexandre Barachant et al. “Multiclass Brain–Computer Interface Classification by Riemannian Geometry”. In: IEEE Transactions on Biomedical Engineering 59.4 (2012), pp. 920–928 12/32
19. Problematics Go beyond x ∼ N(0, Σ) • xi ∼ N(µ, τiΣ) for non-centered data and robustness, • xi ∼ N(0, τi UUT + Ip) for high dimensional data and robustness. Problems • Existence of maximum likelihood estimators ? • Not always closed form estimators: how to get fast iterative algo. ? • Not always closed form expression of the Riemannian distance: what to do ? • How to get fast estimators of centers of mass ? 13/32
20. Estimation and classification of non centered and heteroscedastic data
21. Non-centered mixtures of scaled Gaussian distributions Step 1: data extraction Step 2: feature estimation Step 3: feature classification/regression {xi}n i=1 min θ∈M L(θ, x1, ..., xn) θ1 + θ2 + θ3 + θ4 + θ5 + E.g : one θ characterizes one pixel in remote sensing or one trial in neuroscience ! θ6 + θ8 + θ9 + Example with 2 classes: white and red Non-centered mixtures of scaled Gaussian distributions (NC- MSGs) Let x1, · · · , xn ∈ Rp distributed as xi ∼ N(µ, τiΣ) with µ ∈ Rp, Σ ∈ S++ p , and τ ∈ (R+ ∗ )n. Goal: estimate and classify θ = (µ, Σ, τ). Interesting when data are heteroscedastic (e.g. time series) and/or contain outliers. A. Collas et al. “Riemannian optimization for non-centered mixture of scaled Gaussian distributions” in IEEE Trans. on Signal Processing 2023 14/32
22. Parameter space and cost functions Parameter space: location, scatter matrix, and textures Mp,n = Rp × S++ p × S(R+ ∗ )n where S(R+ ∗ )n = τ ∈ (R+ ∗ )n : n i=1 τi = 1 • Positivity constraints: Σ 0, τi > 0 • Scale constraint: n i=1 τi = 1 Need generic optimization algorithms on Mp,n. Parameter estimation Minimization of a regularized negative log-likelihood (NLL), β ≥ 0 minimize θ∈Mp,n L θ| {xi}n i=1 + βRκ(θ) Center of mass estimation Averaging parameters {θi}M i=1 with a to be defined divergence δ minimize θ∈Mp,n 1 M M i=1 δ(θ, θi) A. Collas et al. “Riemannian optimization for non-centered mixture of scaled Gaussian distributions” in IEEE Trans. on Signal Processing 2023 15/32
23. Parameter space with a product metric Product metric Let ξ = (ξµ , ξΣ , ξτ ), η = (ηµ , ηΣ , ητ ) in the tangent space, ξ, η MProd. p,n θ = ξT µ ηµ + Tr(Σ−1ξΣ Σ−1ηΣ ) + (ξτ τ⊙−1)T(ητ τ⊙−1) where is the elementwise operator. Product manifold =⇒ easy derivation of the geometric tools =⇒ Riemannian gradient descent and conjugate gradient on Mp,n, ., . MProd. p,n . . Slow in practice ... 1 10 100 1000 1 3 Iterations NLL Prod., steepest Prod., conjugate 1 10 100 1000 10−5 10−4 10−3 10−2 10−1 100 Iterations Gradient norm A. Collas et al. “Riemannian optimization for non-centered mixture of scaled Gaussian distributions” in IEEE Trans. on Signal Processing 2023 16/32
24. Parameter space with the Fisher information metric Fisher information metric of NC-MSGs Let ξ = (ξµ , ξΣ , ξτ ), η = (ηµ , ηΣ , ητ ) in the tangent space, ξ, η MFIM p,n θ = n i=1 1 τi ξT µ Σ−1ηµ + n 2 Tr(Σ−1ξΣ Σ−1ηΣ ) + p 2 (ξτ τ⊙−1)T(ητ τ⊙−1) Most geometric tools remain unknown like geodesics, distance ... But, derivation of the Riemannian gradient and a second order retraction. =⇒ Riemannian gradient descent on Mp,n, ., . MFIM p,n . . 1 10 100 1000 1 3 Iterations NLL Prod., steepest Prod., conjugate FIM, steepest 1 10 100 1000 10−5 10−4 10−3 10−2 10−1 100 Iterations Gradient norm A. Collas et al. “Riemannian optimization for non-centered mixture of scaled Gaussian distributions” in IEEE Trans. on Signal Processing 2023 17/32
25. Parameter estimation: existence Observation of sequences (θ(ℓ))ℓ such that L ( θ(ℓ+1) ) < L ( θ(ℓ) ) and θ(ℓ) − − − − → ℓ→+∞ ∂θ where ∂θ is a border of Mp,n (e.g. τi = 0). Existence of a regularized maximum likelihood estimator Under some assumptions on Rκ and β > 0, the regularized NLL θ → L (θ|{xi}n i=1 ) + βRκ (θ), admits a minimum in Mp,n . Example: Rκ (θ) = ∑ i,j ( (τiλj)−1 − κ−1 )2 where λj are the eigenvalues of Σ. A. Collas et al. “Riemannian optimization for non-centered mixture of scaled Gaussian distributions” in IEEE Trans. on Signal Processing 2023 18/32
26. Classification KL divergence between NC-MSGs δKL(θ1, θ2) ∝ n i=1 τ1,i τ2,i Tr Σ−1 2 Σ1 + n i=1 1 τ2,i ∆µTΣ−1 2 ∆µ + n log |Σ2| |Σ1| with ∆µ = µ2 − µ1 . Symmetrization: δMp,n (θ1, θ2) = 1 2 (δKL(θ1, θ2) + δKL(θ2, θ1)). Riemannian center of mass Minimization of the KL variance: minimize θ∈Mp,n 1 M M i=1 δMp,n (θ, θi) Done with a Riemannian gradient descent. 1 10 100 1000 100 101 102 103 104 105 106 Iterations KL variance Prod., steepest Prod., conjugate FIM, steepest Figure 10: KL variance vs. iterations with p = 10, n = 150 and M = 2. A. Collas et al. “Riemannian optimization for non-centered mixture of scaled Gaussian distributions” in IEEE Trans. on Signal Processing 2023 19/32
27. Breizhcrops dataset Breizhcrops dataset1: • more than 600 000 crop time series across the whole Brittany, • 13 spectral bands, 9 classes. January April August December 0 0.2 0.4 0.6 0.8 1 ·104 ρ ρB1 ρB2 ρB3 ρB4 ρB5 ρB6 ρB7 ρB8 ρB8A ρB9 ρB10 ρB12 Sentinel 2 Satellite Spectral Bands Figure 11: Reflectances ρ of a time series of meadows. January April August December 0 0.2 0.4 0.6 0.8 1 ·104 ground signal cloud noise ρ × 104 Figure 12: Reflectances ρ of a time series of corn. 1https://breizhcrops.org/ A. Collas et al. “Riemannian optimization for non-centered mixture of scaled Gaussian distributions” in IEEE Trans. on Signal Processing 2023 20/32
28. Application to the Breizhcrops dataset Parameter estimation + classification with a Nearest centroïd classifier 10−3 10−2 0 0.2 0.4 0.6 t Overall Accuracy X - Euclidean ˆ µSM - Euclidean ˆ ΣSCM - Euclidean ˆ ΣSCM - sym. KL (ˆ µSM, ˆ ΣSCM) - sym. KL θ - sym. KL (a) Mean transformation: xi → xi + µ(t) with µ(0) = 0 10−3 10−2 0 0.2 0.4 0.6 t Overall Accuracy (b) Rotation transformation: xi → Q(t)Txi with Q(0) = Ip Figure 13: “Overall Accuracy” metric versus the parameter t associated with transformations applied to the test set. The proposed Nearest centroïd classifier is “θ - sym. KL”. The regularization is the L2 penalty and β = 10−11. A. Collas et al. “Riemannian optimization for non-centered mixture of scaled Gaussian distributions” in IEEE Trans. on Signal Processing 2023 21/32
29. Probabilistic PCA from heteroscedastic signals
30. Study of a “low rank” statistical model Step 1: data extraction Step 2: feature estimation Step 3: feature classification/regression {xi}n i=1 min θ∈M L(θ, x1, ..., xn) θ1 + θ2 + θ3 + θ4 + θ5 + E.g : one θ characterizes one pixel in remote sensing or one trial in neuroscience ! θ6 + θ8 + θ9 + Example with 2 classes: white and red Statistical model x1, · · · , xn ∈ Rp, ∀k < p: xi ∼ N(0, τi UUT + Ip) with τi > 0 and U ∈ Rp×k is an orthogonal basis (UTU = Ik ). Goal: estimate and classify θ = (U, τ). A. Collas et al. “Probabilistic PCA From Heteroscedastic Signals: Geometric Framework and Application to Clustering” in IEEE Trans. on Signal Processing 2021 22/32
31. Study of a “low rank” statistical model Statistical model xi ∈Rp d = √ τiUg signal∈span(U) + n noise∈Rp where g ∼ N(0, Ik) ⊥ n ∼ N(0, Ip), τ ∈ (R+ ∗ )n, and U ∈ Rp×k s.t. UTU = Ik . Maximum likelihood estimation Minimization of the NLL with constraints, θ = (U, τ) • U ∈ Grp,k : orthogonal basis of the subspace (and thus invariant by rotation !) • τ ∈ (R+ ∗ )n : positivity constraints minimize θ∈Grp,k×(R+ ∗ )n L(θ| {xi}n i=1 ) A. Collas et al. “Probabilistic PCA From Heteroscedastic Signals: Geometric Framework and Application to Clustering” in IEEE Trans. on Signal Processing 2021 23/32
32. Study of a “low rank” statistical model: estimation Fisher information metric ∀ξ = (ξU, ξτ ) , η = (ηU, ητ ) in the tangent space ξ, η FIM θ = 2ncτ Tr ξT UηU + k ξτ (1 + τ)⊙−1 T ητ (1 + τ)⊙−1 , where cτ = 1 n n i=1 τ2 i 1 + τi . Derivation of the Riemannian gradient and of a retraction. To minimize the NLL: Riemannian gradient descent on Grp,k × (R+ ∗ )n, ., . FIM . . 0 50 100 150 200 0 1 2 3 Iterations NLL Prod. metric FIM Figure 14: NLL versus the iterations. A. Collas et al. “Probabilistic PCA From Heteroscedastic Signals: Geometric Framework and Application to Clustering” in IEEE Trans. on Signal Processing 2021 24/32
33. Study of a “low rank” statistical model: bounds Intrinsic Cramér-Rao bounds Study of the performance through intrinsic Cramér-Rao bounds: subspace estimation error E[d2 Grp,k (span(ˆ U), span(U))] ≥ (p − k)k ncτ ≈ (p − k)k n × SNR E[d2 (R+ ∗ )n (ˆ τ, τ)] texture estimation error ≥ 1 k n i=1 (1 + τi)2 τ2 i 103 104 10−1 100 101 Number of samples d2 Grp,k (span(ˆ U), span(U)) SCM BCD RGD ICRB Figure 15: Mean squared error versus the number of simulated data. A. Collas et al. “Probabilistic PCA From Heteroscedastic Signals: Geometric Framework and Application to Clustering” in IEEE Trans. on Signal Processing 2021 25/32
34. Aligning M/EEG data to enhance predictive regression modeling
35. Generative model for regression with M/EEG Linear instantaneous mixing model (from Maxwell’s equations) Signal h(t) ∼ N(0, Σ): h(t) observed signal = A η(t) latent sources Covariance matrix: Σ = Et [h(t)h(t)] = A diag(p)A⊤ with p = Var(η(t)). Regression model, (Σi, yi)n i=1 ∃β ∈ Rp s.t.: yi = β⊤ log(pi) + εi Neglecting εi, ∃β′ s.t. yi = β′⊤ vec log(Σ− 1 2 ΣiΣ− 1 2 ) ∈TIS++ p where Σ is the Riemannian mean of {Σi}n i=1 . David Sabbagh et al. “Manifold-regression to predict from MEG/EEG brain signals without source modeling”. In: Advances in Neural Information Processing Systems 32 (2019) A. Mellot, A. Collas et al. “Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modeling” in Imaging Neuroscience MIT Press 2023 26/32
36. Statistics on the SPD manifold Gaussian distribution on S++ p and normalization f (Σ; Σ, σ2) = 1 Z(σ) exp  − d2 S++ p (Σ, Σ) 2σ2   with Z(σ) the normalization constant. If Σ ∼ f (.; Σ, σ2), then ϕ Σ,σ2 (Σ) = Σ− 1 2 ΣΣ− 1 2 1 σ ∼ f (.; I, 1). Salem Said et al. “Riemannian Gaussian Distributions on the Space of Symmetric Positive Definite Matrices”. In: IEEE Transactions on Information Theory 63.4 (2017), pp. 2153–2170 Estimation with (Σi)n i=1 ∼ f (.; Σ, σ2) ˆ Σ = arg min Σ∈S++ p 1 n n i=1 d2 S++ p (Σ, Σi), ˆ σ2 = 1 n n i=1 d2 S++ p (ˆ Σ, Σi) Domain adaptation: for D ∈ {S, T } ΣD i ← ϕˆ Σ D ,(ˆ σ2)D ΣD i A. Mellot, A. Collas et al. “Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modeling” in Imaging Neuroscience MIT Press 2023 27/32
37. Results on MEG data Brain age prediction on the Cam-CAN dataset: 0 0.2 0.4 0.6 0.8 No alignment z-score Recenter Rescale A rest passive 0 0.2 0.4 0.6 0.8 B rest smt 0 0.2 0.4 0.6 0.8 C passive smt R2 Figure 16: R2 score on the Cam-CAN dataset (MEG), n = 646, 306 channels reduced to p = 65 after PCA and age range of 18 − 89 years old. A. Mellot, A. Collas et al. “Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modeling” in Imaging Neuroscience MIT Press 2023 28/32
38. Results on EEG datasets: LEMON → TUAB Brain age prediction on the LEMON → TUAB datasets, regression on supervised SPoC components: diag(log(WSPoCΣiW⊤ SPoC )). 5 0.50 1.00 0.75 0.50 0.25 0.00 0.25 0.50 R2 No alignment z-score Re-center Re-scale B SPoC Figure 17: Left: R2 score on LEMON (n = 1385) → TUAB (n = 213) (EEG), and p = 15 after PCA. Dashed line is the R2 score of a cross-validation on target dataset. Right: topomaps of the SPoC patterns. Many other results in the paper: simulations, rotation corrections, ... A. Mellot, A. Collas et al. “Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modeling” in Imaging Neuroscience MIT Press 2023 29/32
39. Open source software and conclusions
40. Open source software Step 1: data extraction Step 2: feature estimation Step 3: feature classification/regression {xi}n i=1 min θ∈M L(θ, x1, ..., xn) θ1 + θ2 + θ3 + θ4 + θ5 + E.g : one θ characterizes one pixel in remote sensing or one trial in neuroscience ! θ6 + θ8 + θ9 + Example with 2 classes: white and red pyCovariance (creator) • _FeatureArray: custom data structure to store batch of points of product manifolds, • implements statistical manifolds from this presentation, • automatic computation of Riemannian centers of mass using exp/log or autodiff • K-means++ and Nearest centroïd classifier on any Riemannian manifolds, • 15K lines of code, 96% of test coverage. 30/32
41. Open source software pyManopt (maintainer) minimize θ∈M f (θ) Provide f smooth, choose a Riemannian manifold M, and pyManopt does the rest ! Geomstats: information geometry module (co-creator) Choose a statistical manifold M (or give a p.d.f. !), and Geomstats does the rest: geodesics, log, exp, barycenter, leaning: K-means, KNN, PCA, etc... A. Le Brigant, J. Deschamps, A. Collas and N. Miolane, “Parametric information geometry with the package Geomstats” ACM Transactions on Mathematical Software 2023. 31/32
42. References
43. References Barachant, Alexandre, Stéphane Bonnet, Marco Congedo, and Christian Jutten. “Multiclass Brain–Computer Interface Classification by Riemannian Geometry”. In: IEEE Transactions on Biomedical Engineering 59.4 (2012), pp. 920–928. Boumal, Nicolas. An introduction to optimization on smooth manifolds. Cambridge University Press, 2023. Collas, Antoine, Florent Bouchard, Arnaud Breloy, Guillaume Ginolhac, Chengfang Ren, and Jean-Philippe Ovarlez. “Probabilistic PCA from heteroscedastic signals: geometric framework and application to clustering”. In: IEEE Transactions on Signal Processing 69 (2021), pp. 6546–6560. Collas, Antoine, Arnaud Breloy, Chengfang Ren, Guillaume Ginolhac, and Jean-Philippe Ovarlez. “Riemannian optimization for non-centered mixture of scaled Gaussian distributions”. In: IEEE Transactions on Signal Processing (2023). Le Brigant, Alice, Jules Deschamps, Antoine Collas, and Nina Miolane. “Parametric information geometry with the package Geomstats”. In: ACM Transactions on Mathematical Software (2022). Mellot, Apolline, Antoine Collas, Pedro L. C. Rodrigues, Denis Engemann, and Alexandre Gramfort. “Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modeling”. In: Imaging Neuroscience (Nov. 2023). issn: 2837-6056. doi: 10.1162/imag_a_00040. Sabbagh, David, Pierre Ablin, Gaël Varoquaux, Alexandre Gramfort, and Denis A Engemann. “Manifold-regression to predict from MEG/EEG brain signals without source modeling”. In: Advances in Neural Information Processing Systems 32 (2019). Said, Salem, Lionel Bombrun, Yannick Berthoumieu, and Jonathan H. Manton. “Riemannian Gaussian Distributions on the Space of Symmetric Positive Definite Matrices”. In: IEEE Transactions on Information Theory 63.4 (2017), pp. 2153–2170. 32/32
44. Riemannian geometry for statistical estimation and learning: applications to remote sensing and M/EEG Antoine Collas S3 seminar - L2S, CentraleSupélec