Publications associées à The Effect of the Intrinsic Dimension on the Generalization of Quadratic Classifiers

Publications associées (16)

Exploring the heterogeneous morphometric data in essential tremor with probabilistic modelling

Dimitri Nestor Alice Van De Ville, Constantin Tuleasca

Essential tremor (ET) is a prevalent movement disorder characterized by marked clinical heterogeneity. Here, we explored the morphometric underpinnings of this cross-subject variability on a cohort of 34 patients with right -dominant drug-resistant ET and ...

2023

Optimal denoising of rotationally invariant rectangular matrices

Lenka Zdeborová, Florent Gérard Krzakala, Emanuele Troiani, Vittorio Erba

In this manuscript we consider denoising of large rectangular matrices: given a noisy observation of a signal matrix, what is the best way of recovering the signal matrix itself? For Gaussian noise and rotationally-invariant signal priors, we completely ch ...

2022

Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

Parviz Ghaderi

The performance of myoelectric control highly depends on the features extracted from surface electromyographic (sEMG) signals. We propose three new sEMG features based on the kernel density estimation. The trimmed mean of density (TMD), the entropy of dens ...

FRONTIERS MEDIA SA2022

SPHARMA approximations for stationary functional time series on the sphere

Alessia Caponera

In this paper, we focus on isotropic and stationary sphere-cross-time random fields. We first introduce the class of spherical functional autoregressive-moving average processes (SPHARMA), which extend in a natural way the spherical functional autoregressi ...

2021

Variably Scaled Kernels Improve Classification of Hormonally-Treated Patient-Derived Xenografts

Cathrin Brisken, Fabio De Martino, Marie Shamseddin

Little is known about the biological functions which are exerted by hormone receptors in physiological conditions. Here, we made use of the Mouse INtraDuctal (MIND) model, an innovative patient-derived xenograft (PDX) model, to characterize global gene exp ...

IEEE2020

Uncertainty and sensitivity analysis of PWR mini-core transients in the presence of nuclear data uncertainty using non-parametric tolerance limits

Andreas Pautz, Alexander Aures

The impact of nuclear data uncertainties is studied for the reactor power and the reactivity during control rod withdrawal transients with reactivity insertions of 0.5

and 0.97

, respectively, for a PWR mini-core model. Multi-group cross sections, the mul ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Graph Laplacian Mixture Model

Pascal Frossard, Hermina Petric Maretic

Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all avai ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Wasserstein Distributionally Robust Learning

Soroosh Shafieezadeh Abadeh

Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...

EPFL2020

Efficient Statistical Parameter Selection for Nonlinear Modeling of Process/Performance Variation

Giovanni De Micheli, Pierre-Emmanuel Julien Marc Gaillardon, Hassan Ghasemzadeh Mohammadi

With the growing number of process variation sources in deeply nano-scaled technologies, parameterized device and circuit modeling is becoming very important for chip design and verification. However, the high dimensionality of parameter space, for process ...

Ieee-Inst Electrical Electronics Engineers Inc2016

CP-CHARM: Segmentation-Free Image Classification Made Accessible

Virginie Sophie Uhlmann

Background: Automated classification using machine learning often relies on features derived from segmenting individual objects, which can be difficult to automate. WND-CHARM is a previously developed classification algorithm in which features are computed ...

2016

Graph Embedded Nonparametric Mutual Information For Supervised Dimensionality Reduction

Nikolaos Arvanitopoulos Darginis

In this paper, we propose a novel algorithm for dimensionality reduction that uses as a criterion the mutual information (MI) between the transformed data and their cor- responding class labels. The MI is a powerful criterion that can be used as a proxy to ...

Institute of Electrical and Electronics Engineers2015

Learning with tensors: a framework based on convex optimization and spectral regularization

Quoc Tran Dinh

We present a framework based on convex optimization and spectral regularization to perform learning when feature observations are multidimensional arrays (tensors). We give a mathematical characterization of spectral penalties for tensors and analyze a uni ...

Springer2014

Learning Discriminative Features and Structured Models for Segmentation in Microscopy and Natural Images

Aurélien Lucchi

Segmenting images is a significant challenge that has drawn a lot of attention from different fields of artificial intelligence and has many practical applications. One such challenge addressed in this thesis is the segmentation of electron microscope (EM) ...

EPFL2013

Second-Order Comparison of Gaussian Random Functions and the Geometry of DNA Minicircles

Victor Panaretos, John Maddocks, David Kraus

Given two samples of continuous zero-mean iid Gaussian processes on [0,1], we consider the problem of testing whether they share the same covariance structure. Our study is motivated by the problem of determining whether the mechanical properties of short ...

2010

Impulse Radio Ultra-Wideband Ranging Algorithm under Multi User Environments

Jean-Yves Le Boudec

Practical impulse radio ultra-wideband (IR-UWB) ranging systems always have to work in multi-user and weak non-line-of-sight (NLOS) environments. In this paper, we derive a novel IR-UWB ranging estimator under multi-user and weak NLOS environments. We mode ...

IEEE2009

Hypothesis testing for evaluating a multimodal pattern recognition framework applied to speaker detection

Murat Kunt, Patricia Besson

Background: Speaker detection is an important component of many human-computer interaction applications, like for example, multimedia indexing, or ambient intelligent systems. This work addresses the problem of detecting the current speaker in audio-visual ...

BioMed Central2008