pyCP_APR is a Python library for tensor decomposition and anomaly detection that is developed as part of the R&D 100 award wining SmartTensors project. It is designed for the fast analysis of large datasets by accelerating computation speed using GPUs. pyCP_APR uses the CANDECOMP/PARAFAC Alternating Poisson Regression (CP-APR) tensor factorization algorithm utilizing both Numpy and PyTorch backend. While the Numpy backend can be used for the analysis of both sparse and dense tensors, PyTorch backend provides faster decomposition of large and sparse tensors on the GPU. pyCP_APR’s Scikit-learn like API allows comfortable interaction with the library, and include the methods for anomaly detection via the p-values obtained from the CP-APR factorization.


  author = {M. E. {Eren} and J. S. {Moore} and E. {Skau} and M. {Bhattarai} and G. {Chennupati} and B. S. {Alexandrov}},
  title = {pyCP\_APR},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4840598},
  howpublished = {\url{\_APR}}

  author={M. E. {Eren} and J. S. {Moore} and B. S. {Alexandrov}},
  booktitle={2020 IEEE International Conference on Intelligence and Security Informatics (ISI)},
  title={Multi-Dimensional Anomalous Entity Detection via Poisson Tensor Factorization},
Maksim E. Eren
Maksim E. Eren

My research interests lie at the intersection of the machine learning and cybersecurity disciplines, with a concentration in tensor decomposition.