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Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review

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arxiv 1905.02845 v1 pith:R3QGYMZW submitted 2019-05-07 cs.LG cs.AIcs.CVstat.ML

classification cs.LGcs.AIcs.CVstat.ML
keywords featureextractionmethodsselectionanalysisdataextractingfeatures
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Pattern analysis often requires a pre-processing stage for extracting or selecting features in order to help the classification, prediction, or clustering stage discriminate or represent the data in a better way. The reason for this requirement is that the raw data are complex and difficult to process without extracting or selecting appropriate features beforehand. This paper reviews theory and motivation of different common methods of feature selection and extraction and introduces some of their applications. Some numerical implementations are also shown for these methods. Finally, the methods in feature selection and extraction are compared.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review

    cs.LG 2025-06 accept novelty 4.0 of 10

    The paper's new contribution is a taxonomy that organizes small-data ML methods by ML lifecycle stage rather than by technique.

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