Dimensionality reduction

Linguistics

Philosophy

Semi-supervised learning

Feature learning

Theoretical computer science

Curse of dimensionality

Selection (genetic algorithm)

Computer science

Supervised learning

Graph

Pattern recognition (psychology)

Mathematics

Feature selection

Feature (linguistics)

Mathematical analysis

Artificial intelligence

Artificial neural network

Unsupervised learning

Generalization

Machine learning

Feature selection aims to reduce dimensionality for building comprehensible learning models with good generalization performance. Feature selection algorithms are largely studied separately according to the type of learning: supervised or unsupervised. This work exploits intrinsic properties underlying supervised and unsupervised feature selection algorithms, and proposes a unified framework for feature selection based on spectral graph theory. The proposed framework is able to generate families of algorithms for both supervised and unsupervised feature selection. And we show that existing powerful algorithms such as ReliefF (supervised) and Laplacian Score (unsupervised) are special cases of the proposed framework. To the best of our knowledge, this work is the first attempt to unify supervised and unsupervised feature selection, and enable their joint study under a general framework. Experiments demonstrated the efficacy of the novel algorithms derived from the framework.

Spectral feature selection for supervised and unsupervised learning