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Working with large amounts of data in machine learning can be a tedious task. It takes an unnecessary amount of time and storage and a lot of the input data is often redundant. This is where feature extraction comes in.
Feature extraction is a technique used to reduce a large input data set into relevant features. This is done with dimensionality reduction to transform large input data into smaller, meaningful groups for processing.
Feature extraction can prove helpful when training a machine learning model. It leads to:
Due to its multiple benefits, feature extraction plays an important role in many areas, such as:
The following is a list of some common feature extraction techniques used by the Machine Learning community: