Anomaly detection via adaptive greedy model
Anomaly detection via adaptive greedy model is a scholarly work, published in 2019 in ''Neurocomputing''. The main subjects of the publication include anomaly detection, intrusion detection system, data mining, water distribution system, artificial intelligence, computer science, algorithm, greedy algorithm, constraint, benchmark, norm, autoencoder, pattern recognition, feature selection, and Sparse modeling. The authors propose a dictionary selection model based on ℓ2, 0 norm constraint to select an optimal small subset of the training data to construct a condense dictionary, which can improve accuracy and reduce computational burden simultaneously.