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Found 10 papers, 4 papers with code
Sparse PCA with False Discovery Rate Controlled Variable Selection
Sparse principal component analysis (PCA) aims at mapping large dimensional data to a linear subspace of lower dimension.
Convex Parameter Estimation of Perturbed Multivariate Generalized Gaussian Distributions
In robust estimation, imposing classical constraints on the precision matrix, such as sparsity, has been limited by the non-convexity of the resulting cost function.
Choosing the parameter of the Fermat distance: navigating geometry and noise
The Fermat distance has been recently established as a useful tool for machine learning tasks when a natural distance is not directly available to the practitioner or to improve the results given by Euclidean distances by exploding the geometrical and statistical properties of the dataset.
A Robust and Flexible EM Algorithm for Mixtures of Elliptical Distributions with Missing Data
To overcome this issue, a new EM algorithm is investigated for mixtures of elliptical distributions with the property of handling potential missing data.
Robust classification with flexible discriminant analysis in heterogeneous data
Linear and Quadratic Discriminant Analysis are well-known classical methods but can heavily suffer from non-Gaussian distributions and/or contaminated datasets, mainly because of the underlying Gaussian assumption that is not robust.
PCA-based Multi Task Learning: a Random Matrix Approach
The article proposes and theoretically analyses a \emph{computationally efficient} multi-task learning (MTL) extension of popular principal component analysis (PCA)-based supervised learning schemes \cite{barshan2011supervised, bair2006prediction}.
Riemannian classification of EEG signals with missing values
This paper proposes a strategy to handle missing data for the classification of electroencephalograms using covariance matrices.
Properties of a new $R$-estimator of shape matrices
This paper aims at presenting a simulative analysis of the main properties of a new $R$-estimator of shape matrices in Complex Elliptically Symmetric (CES) distributed observations.
Robust Semiparametric Efficient Estimators in Elliptical Distributions
The class of elliptical distributions can be seen as a semiparametric model where the finite-dimensional vector of interest is given by the location vector and by the (vectorized) covariance/scatter matrix, while the density generator represents an infinite-dimensional nuisance function.
A flexible EM-like clustering algorithm for noisy data
Though very popular, it is well known that the EM for GMM algorithm suffers from non-Gaussian distribution shapes, outliers and high-dimensionality.
