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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| 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: |
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I19-R120-10915-56980 |
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dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas dimension reduction variable selection dependence measures supervised learning |
| spellingShingle |
Ciencias Informáticas dimension reduction variable selection dependence measures supervised learning Tomassi, Diego Marx, Nicolás Beauseroy, Pierre Feature extraction and selection using statistical dependence criteria |
| topic_facet |
Ciencias Informáticas dimension reduction variable selection dependence measures supervised learning |
| description |
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. |
| format |
Objeto de conferencia Objeto de conferencia |
| author |
Tomassi, Diego Marx, Nicolás Beauseroy, Pierre |
| author_facet |
Tomassi, Diego Marx, Nicolás Beauseroy, Pierre |
| author_sort |
Tomassi, Diego |
| title |
Feature extraction and selection using statistical dependence criteria |
| title_short |
Feature extraction and selection using statistical dependence criteria |
| title_full |
Feature extraction and selection using statistical dependence criteria |
| title_fullStr |
Feature extraction and selection using statistical dependence criteria |
| title_full_unstemmed |
Feature extraction and selection using statistical dependence criteria |
| title_sort |
feature extraction and selection using statistical dependence criteria |
| publishDate |
2016 |
| url |
http://sedici.unlp.edu.ar/handle/10915/56980 http://45jaiio.sadio.org.ar/sites/default/files/ASAI-13_0.pdf |
| work_keys_str_mv |
AT tomassidiego featureextractionandselectionusingstatisticaldependencecriteria AT marxnicolas featureextractionandselectionusingstatisticaldependencecriteria AT beauseroypierre featureextractionandselectionusingstatisticaldependencecriteria |
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Repositorios |
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1764820476776939522 |