# ECON: a Kernel Basis Pursuit Algorithm with Automatic Feature Parameter Tuning, and its Application to Photometric Solids Approximation

* Auteur correspondant
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe
Abstract : This paper introduces a new algorithm, namely the Equi-Correlation Network (ECON), to perform supervised classification, and regression. ECON is a kernelized LARS-like algorithm, by which we mean that ECON uses an $l_1$ regularization to produce sparse estimators, ECON efficiently rides the regularization path to obtain the estimator associated to any regularization constant values, and ECON represents the data by way of features induced by a feature function. The originality of ECON is that it automatically tunes the parameters of the features while riding the regularization path. So, ECON has the unique ability to produce optimally tuned features for each value of the constant of regularization. We illustrate the remarkable experimental performance of ECON on standard benchmark datasets; we also present a novel application of machine learning in the field of computer graphics, namely the approximation of photometric solids.
Type de document :
Communication dans un congrès
IEEE Press. International Conference on Machine Learning and Applications, Dec 2009, Miami, United States. 2009
Domaine :

Littérature citée [18 références]

https://hal.inria.fr/inria-00430578
Contributeur : Preux Philippe <>
Soumis le : jeudi 8 novembre 2012 - 15:31:02
Dernière modification le : jeudi 11 janvier 2018 - 01:49:33
Document(s) archivé(s) le : samedi 9 février 2013 - 02:30:09

### Fichier

icmla2009.pdf
Fichiers produits par l'(les) auteur(s)

### Identifiants

• HAL Id : inria-00430578, version 1

### Citation

Loth Manuel, Preux Philippe, Delepoulle Samuel, Renaud Christophe. ECON: a Kernel Basis Pursuit Algorithm with Automatic Feature Parameter Tuning, and its Application to Photometric Solids Approximation. IEEE Press. International Conference on Machine Learning and Applications, Dec 2009, Miami, United States. 2009. 〈inria-00430578〉

### Métriques

Consultations de la notice

## 359

Téléchargements de fichiers