SSVDD_Python

Subspace Support Vector Data Description model

A model which projects the positive class data into lower-dimensional feature space and makes hyper-spherical boundary around positive class while keeping the negative class data out of the boundary.

Requirements

The SVDD model is based on Support Vector Data Description model and hence, for implementation purpose, we have used the openly available python implementation of SVDD. Therefore, add this to the directory before implementing SSVDD.

Demo

The demo of SSVDD is given HERE.

Citations

To use any part of this implementation, please cite the following papers.

@inproceedings{sohrab2018subspace,
  title={Subspace support vector data description},
  author={Sohrab, Fahad and Raitoharju, Jenni and Gabbouj, Moncef and Iosifidis, Alexandros},
  booktitle={2018 24th International Conference on Pattern Recognition (ICPR)},
  pages={722--727},
  year={2018},
  organization={IEEE}
}

@article{zaffar2023credit,
  title={Credit Card Fraud Detection with Subspace Learning-based One-Class Classification},
  author={Zaffar, Zaffar and Sohrab, Fahad and Kanniainen, Juho and Gabbouj, Moncef},
  journal={arXiv preprint arXiv:2309.14880},
  year={2023}
}