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
# 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
- ICA. See AI/Unsupervised learning/Unsupervised learning
- 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
# 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