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Feature extraction and selection using statistical dependence criteria

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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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Dates and versions

hal-03320930 , version 1 (16-08-2021)

Identifiers

  • HAL Id : hal-03320930 , version 1

Cite

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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