Feature extraction and selection using statistical dependence criteria
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...
Guardado en:
| Autores principales: | , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2016
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/56980 http://45jaiio.sadio.org.ar/sites/default/files/ASAI-13_0.pdf |
| Aporte de: |
| Sumario: | 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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