Principal Components Analysis Algorithm

Principal Component Analysis (PCA) is one of the most famous models used for dimensionality reduction. It uses an orthonormal transformation to convert a set of observation of possibly correlated variables into a set of linearly uncorrelated variables. It corresponds to a feature...

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Feature Extraction

Feature extraction techniques explore the relationships and dependencies between features to create a new dataset of transformed features. Unlike feature selection methods that only select the best features from the existing ones, feature extraction models go further...

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Feature Selection

Feature selection, also known as variable or attribute selection, is the process of selecting a subset with the most relevant or useful features for use in  a model construction.  Instead of creating new features, feature selection aims to identify the most discriminative...

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