Publications related to Sparsest piecewise-linear regression of one-dimensional data

Structured Dimensionality Reduction for Additive Model Regression

Additive models are regression methods which model the response variable as the sum of univariate transfer functions of the input variables. Key benefits of additive models are their accuracy and interpretability on many real-world tasks. Additive models a ...

Institute of Electrical and Electronics Engineers2016

Predicting Individual Scores From Resting State fMRI Using Partial Least Squares Regression

Dimitri Nestor Alice Van De Ville, Stephan Morgenthaler, Thomas William Arthur Bolton, Maria Giulia Preti, Djalel Eddine Meskaldji

An important question in neuroscience is to reveal the relationship between individual performance and brain activity. This could be achieved by applying model regression techniques, in which functional connectivity derived from resting-state functional ma ...

Ieee2016

Consistency of $\ell_1$-Regularized Maximum-Likelihood for Compressive Poisson Regression

Volkan Cevher, Yen-Huan Li

We consider Poisson regression with the canonical link function. This regression model is widely used in regression analysis involving count data; one important application in electrical engineering is transmission tomography. In this paper, we establish t ...

2015

Regression Analysis of Building Energy Demand

This project attempts to tackle the problem of analysing the heat demand of build- ings at a large (district or city) scale. Specifically, building data from Geneva is used as a case study while building an environment with suitable tools for a continued a ...

2015

Methodologies for predicting natural frequency variation of a suspension bridge

Ian Smith, Ngoc Thanh Trinh, Irwanda Laory

In vibration-based structural health monitoring, changes in the natural frequency of a structure are used to identify changes in the structural conditions due to damage and deterioration. However, natural frequency values also vary with changes in environm ...

Elsevier2014

Micro-Structural Brain Alterations in Aviremic HIV+ Patients with Minor Neurocognitive Disorders: A Multi-Contrast Study at High Field

Tobias Kober, Reto Meuli, Alessandro Daducci, Cristina Granziera, Djalel Eddine Meskaldji, Gunnar Krüger, Alexis Roche, François Lazeyras

Objective Mild neurocognitive disorders (MND) affect a subset of HIV+ patients under effective combination antiretroviral therapy (cART). In this study, we used an innovative multi-contrast magnetic resonance imaging (MRI) approach at high-field to assess ...

Public Library of Science2013

Compressible distributions for high-dimensional statistics

Volkan Cevher, Rémi Gribonval

We develop a principled way of identifying probability distributions whose independent and identically distributed realizations are compressible, i.e., can be well approximated as sparse. We focus on Gaussian compressed sensing, an example of underdetermin ...

Institute of Mathematical Statistics2012

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

Volkan Cevher, Anastasios Kyrillidis

The least absolute shrinkage and selection operator (LASSO) for linear regression exploits the geometric interplay of the

\ell_2

-data error objective and the

\ell_1

-norm constraint to arbitrarily select sparse models. Guiding this uninformed selection ...

IEEE2012

An Adaptive Approach for Online Segmentation of Multi-Dimensional Mobile Data

Karl Aberer, Zhixian Yan, Tian Guo

With increasing availability of mobile sensing devices including smartphones, online mobile data segmentation becomes an important topic in reconstructing and understanding mobile data. Traditional approaches like online time series segmentation either use ...

2012

Chebyshev polynomial approximation for transductive learning on graphs

Nathanaël Perraudin

The goal of transductive learning is to find a way to recover the labels of lots of data with only a few known samples. In this work, we will work on graphs for two reasons. First, it’s possible to construct a graph from a given dataset with features. The ...

2011

Sparsest piecewise-linear regression of one-dimensional data

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Structured Dimensionality Reduction for Additive Model Regression

Predicting Individual Scores From Resting State fMRI Using Partial Least Squares Regression

Consistency of $\ell_1$-Regularized Maximum-Likelihood for Compressive Poisson Regression

Regression Analysis of Building Energy Demand

Methodologies for predicting natural frequency variation of a suspension bridge

Micro-Structural Brain Alterations in Aviremic HIV+ Patients with Minor Neurocognitive Disorders: A Multi-Contrast Study at High Field

Compressible distributions for high-dimensional statistics

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm

An Adaptive Approach for Online Segmentation of Multi-Dimensional Mobile Data

Chebyshev polynomial approximation for transductive learning on graphs

Compressible distributions for high-dimensional statistics

Chebyshev polynomial approximation for transductive learning on graphs

Regression Analysis of Building Energy Demand

Consistency of $\ell_1$-Regularized Maximum-Likelihood for Compressive Poisson Regression

Micro-Structural Brain Alterations in Aviremic HIV+ Patients with Minor Neurocognitive Disorders: A Multi-Contrast Study at High Field

Predicting Individual Scores From Resting State fMRI Using Partial Least Squares Regression

Structured Dimensionality Reduction for Additive Model Regression

An Adaptive Approach for Online Segmentation of Multi-Dimensional Mobile Data

Methodologies for predicting natural frequency variation of a suspension bridge

Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm