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Feature extraction and selection using statistical dependence criteria

Abstract : Dimensionality reduction using feature extraction and selection approaches is a common stage of many regression and classification tasks. In recent years there have been significant e orts to reduce the dimension of the feature space without lossing information that is relevant for prediction. This objective can be cast into a conditional independence condition between the response or class labels and the transformed features. Building on this, in this work we use measures of statistical dependence to estimate a lower-dimensional linear subspace of the features that retains the su cient information. Unlike likelihood-based and many momentbased methods, the proposed approach is semi-parametric and does not require model assumptions on the data. A regularized version to achieve simultaneous variable selection is presented too. Experiments with simulated data show that the performance of the proposed method compares favorably to well-known linear dimension reduction techniques.
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https://hal-utt.archives-ouvertes.fr/hal-03320930
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Monday, August 16, 2021 - 4:47:58 PM
Last modification on : Wednesday, August 25, 2021 - 3:27:48 AM

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  • HAL Id : hal-03320930, version 1

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Diego Tomassi, Nicolas Marx, Pierre Beauseroy. Feature extraction and selection using statistical dependence criteria. ASAI - Simposio Argentino de Inteligencia Artificial (17º edición), Sep 2016, Buenos Aires, Argentina. ⟨hal-03320930⟩

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