What is feature extraction?

Overview

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.

Benefits

Feature extraction can prove helpful when training a machine learning model. It leads to:

• A Boost in training speed
• An improvement in model accuracy
• A reduction in risk of overfitting
• A rise in model explainability
• Better data visualization

Applications

Due to its multiple benefits, feature extraction plays an important role in many areas, such as:

• Pattern recognition
• Image processing
• The bag of words model in Natural language processing
• Autoencoders in unsupervised learning

Feature extraction techniques

The following is a list of some common feature extraction techniques used by the Machine Learning community:

• Principle Components Analysis (PCA)
• Independent Component Analysis (ICA)
• Linear Discriminant Analysis (LDA)
• Locally Linear Embedding (LLE)
• t-distributed Stochastic Neighbor Embedding (t-SNE)