ECON: a Kernel Basis Pursuit Algorithm with Automatic Feature Parameter Tuning, and its Application to Photometric Solids Approximation - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2009

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

Loth Manuel
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Preux Philippe
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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.
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Dates and versions

inria-00430578 , version 1 (08-11-2012)

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  • HAL Id : inria-00430578 , version 1

Cite

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. International Conference on Machine Learning and Applications, Dec 2009, Miami, United States. ⟨inria-00430578⟩
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