artificial neural networks multilayer perceptrons dictionary learning independent component analysis autoencoders matrix factorization clustering feature perceptrons matrix neural network neural networks
Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data.  Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization and various forms of clustering.
