Feature learning

In ML, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task.

Resources

• https://en.wikipedia.org/wiki/Feature_learning
• Feature Engineering for Machine Learning

Unsupervised case

• When the feature learning is performed in an unsupervised way, it enables a form of semisupervised learning where features learned from an unlabeled dataset are then employed to improve performance in a supervised setting with labeled data
• K-means clustering. See AI/Unsupervised Learning/Clustering#K-means
• ICA. See AI/Unsupervised Learning/Unsupervised learning#Independent component analysis ICA
• Dictionary learning. See AI/Unsupervised Learning/Sparse dictionary learning
• Matrix factorization. See AI/Unsupervised Learning/Dimensionality reduction and low rank modeling
• PCA: AI/Unsupervised Learning/PCA

Multilayer (deep) architectures

• See AI/Deep Learning/Autoencoders

Supervised case

• See AI/Deep Learning/DL
• Representational Learning (RL) refers to learning latent representations using non-parametric (i.e. non-statistical) methods to extract features
• Deep supervised models (Multilayer perceptron, Supervised neural networks) are able to learn automatically features from data