Name
Abstract | ||
|---|---|---|
We propose a one-class neural network (OC-NN) model to detect anomalies in complex data sets. OC-NN combines the ability of deep networks to extract progressively rich representation of data with the one-class objective of creating a tight envelope around normal data. The OC-NN approach breaks new ground for the following crucial reason: data representation in the hidden layer is driven by the OC-NN objective and is thus customized for anomaly detection. This is a departure from other approaches which use a hybrid approach of learning deep features using an autoencoder and then feeding the features into a separate anomaly detection method like one-class SVM (OC-SVM). The hybrid OC-SVM approach is suboptimal because it is unable to influence representational learning in the hidden layers. A comprehensive set of experiments demonstrate that on complex data sets (like CIFAR and PFAM), OC-NN significantly outperforms existing state-of-the-art anomaly detection methods. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Anomaly detection,External Data Representation,Autoencoder,Support vector machine,Complex data type,Artificial intelligence,Artificial neural network,Mathematics,Machine learning |
DocType | Volume | Citations |
Journal | abs/1802.06360 | 13 |
PageRank | References | Authors |
0.52 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raghavendra Chalapathy | 1 | 56 | 3.03 |
| Aditya Krishna Menon | 2 | 709 | 40.01 |
| Sanjay Chawla | 3 | 1372 | 105.09 |