TensorNOODL

A Matlab package for provable structured CP/PARAFAC tensor factorization via dictionary learning for exact recovery of the constituent factors.

Download TensorNOODL

Kindly acknowledge TensorNOODL as

“S. Rambhatla, X. Li, and J. Haupt (2020). Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning. Neural Information Processing Systems (NeurIPS) https://arxiv.org/abs/2006.16442

BibTeX:

@article{rambhatla2020provable,
  title={Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning},
  author={Rambhatla, Sirisha and Li, Xingguo and Haupt, Jarvis},
  journal={Neural Information Processing Systems (NeurIPS)},
  year={2020}
}

NOODL

A scalable Matlab and TensorFlow package for provable online dictionary learning.

Download NOODL

Kindly acknowledge NOODL as

“S. Rambhatla, X. Li, and J. Haupt (2019). NOODL: Provable Online Dictionary Learning and Sparse Coding. International Conference on Learning Representations (ICLR) https://openreview.net/forum?id=HJeu43ActQ

BibTeX:

@inproceedings{rambhatla2018noodl,
  title={NOODL: Provable Online Dictionary Learning and Sparse Coding},
  author={Rambhatla, Sirisha and Li, Xingguo and Haupt, Jarvis},
  booktitle={International Conference on Learning Representations},
  year={2019}
}

Dictionary-based Generalization of Robust PCA (DRPCA)

A Matlab package for demixing a matrix as a superposition of a low-rank component and a dictionary sparse component (multiple sparsity modalities are available) with applications to Target localization in Hyperspectral images.

Download DRPCA

Kindly acknowledge DRPCA as

“S. Rambhatla, X. Li, J. Ren and J. Haupt, “A Dictionary-Based Generalization of Robust PCA With Applications to Target Localization in Hyperspectral Imaging,” in IEEE Transactions on Signal Processing, vol. 68, pp. 1760-1775, 2020, doi: 10.1109/TSP.2020.2977458.”

BibTeX:

@ARTICLE{rambhatlaDRPCA2020,
  author={S. {Rambhatla} and X. {Li} and J. {Ren} and J. {Haupt}},
  journal={IEEE Transactions on Signal Processing}, 
  title={A Dictionary-Based Generalization of Robust PCA With Applications to Target Localization in Hyperspectral Imaging}, 
  year={2020},
  volume={68},
  pages={1760-1775},}

TensorMap

A Matlab package for developing topological maps for vehicle navigation using tensor decompositions.

Download TensorMap

Kindly acknowledge TensorMap as

“S. Rambhatla, N. D. Sidiropoulos and J. Haupt, “TensorMap: Lidar-based Topological Mapping and Localization via Tensor Decompositons},” 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA, 2018, pp. 1368-1372, doi: 10.1109/GlobalSIP.2018.8646665”

BibTeX:

@INPROCEEDINGS{rambhatlaTensorMap2018,
  author={S. {Rambhatla} and N. D. {Sidiropoulos} and J. {Haupt}},
  booktitle={2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)}, 
  title={TensorMap: Lidar-based Topological Mapping and Localization via Tensor Decompositons}, 
  year={2018},
  pages={1368-1372},}

To comment, contribute, or report issues, visit the hosting page at GitHub, or else email me.