Dimensionality Reduction

Abstract

AbstractDimensionality reduction studies methods that effectively reduce data dimensionality for efficient data processing tasks such as pattern recognition, machine learning, text retrieval, and data mining. We introduce the field of dimensionality reduction by dividing it into two parts: feature extraction and feature selection. Feature extraction creates new features resulting from the combination of the original features; and feature selection produces a subset of the original features. Both attempt to reduce the dimensionality of a dataset to facilitate efficient data processing tasks. We introduce key concepts of feature extraction and feature selection, describe some basic methods, and illustrate their applications with some practical cases. Extensive research into dimensionality reduction has being carried out for many decades. Even today its demand is further increasing due to important high‐dimensional applications such as gene expression data, text categorization, and document indexing.