If you find any of the toolboxes offered here useful, please cite the relevant paper(s). If you want to improve or build on any of it, feel free to do so! Some of these codes are also on on bitbucket.

Some of these codes are not maintained very often. If you have any trouble running them, please email me (jm.murphy@tufts.edu) and I will try to assist you or direct you to the person who maintains the code.

• Wasserstein Unmixing for Hyperspectral Images.

  Papers:

  • S. Fullenbaum, M. Mueller, A. Tasissa, and J.M. Murphy. "Hyperspectral Image Clustering Via Learned Representation In Wasserstein Space". IEEE International Symposium on Geoscience and Remote Sensing, 8791-8796. 2024.
  • S. Fullenbaum, M. Mueller, A. Tasissa, and J.M. Murphy. "Nonlinear Unmixing of Hyperspectral Images via Regularized Wasserstein Dictionary Learning". IEEE International Symposium on Geoscience and Remote Sensin, 8289-8294. 2024.

• Fermat Distance Spectral Clustering (FDSC).

  Paper: "Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithm". Journal of Machine Learning Research, 25(176), pp. 1-65, 2024.

  
  

• Superpixel-based and Spatially-regularized Diffusion Learning (S2DL).

  Paper: K. Cui, R. Li, S.L. Polk, Y. Lin, H. Zhang, J.M. Murphy, R.J. Plemmons, and R.H. Chan. "Superpixel-based and Spatially-regularized Diffusion Learning Method for Unsupervised Hyperspectral Image Clustering". IEEE Transactions on Geoscience and Remote Sensing, 62, pp. 1-18, 2024.

  
  

• Locality Regularized Reconstruction.

  Paper: M. Mueller, J.M. Murphy, and A. Tasissa. "Locality Regularized Reconstruction: Structured Sparsity and Delaunay Triangulations" arXiv. 2024.

  
  

• Geometrically Regularized Wasserstein Dictionary Learning (GeoWDL).

  Paper: M. Mueller, S. Aeron, J.M. Murphy, and A. Tasissa. "Geometrically Regularized Wasserstein Dictionary Learning". Topological, Algebraic and Geometric Learning Workshops, PMLR. 2023.

  
  

• Soft Rank Energy (sRE).

  Papers:

  • S. bin Masud, M. Werenski, J.M. Murphy, and S. Aeron. "Multivariate Soft Rank via Entropy-Regularized Optimal Transport: Sample Efficiency and Generative Modeling ". Journal of Machine Learning Research, 24(160), 1-65. 2023.
  • M. Werenski, S. bin Masud, J.M. Murphy, and S. Aeron. "On Rank Energy Statistics via Optimal Transport: Continuity, Convergence, and Change Point Detection". IEEE Transactions on Information Theory, 70(7), 5168-5190, 2024.
  
  

• Diffusion and Volume Maximization-Based Image Clustering (D-VIC).

  Paper: S.L. Polk, K. Cui, A. Chan, D. Coomes, R.J. Plemmons, and J.M. Murphy. "Unsupervised Diffusion and Volume Maximization-based Clustering Hyperspectral Images". Remote Sensing, 15(4), 1053. 2023.

  
  

• K-Deep Simplex (KDS).

  Paper: A. Tasissa, P. Tankala, J.M. Murphy, and D. Ba. "K-Deep Simplex: Manifold Learning via Local Dictionaries". IEEE Transactions on Signal Processing, 71, 3741-3754. 2023




• Barycentric Coding Model (BCM).

  Paper: M. Werenski, R. Jiang, A. Tasissa, S. Aeron, and J.M. Murphy. "Measure Estimation in the Barcyentric Coding Model". Proceedings of the 39th International Conference on Machine Learning, Baltimore, Maryland, USA, PMLR 162:23781-23803, 2022.

  
  

• Multiscale Learning by Unsupervised Nonlinear Diffusion.

  Paper: J.M. Murphy and S.L. Polk. "A Multiscale Environment for Learning by Diffusion". Applied and Computational Harmonic Analysis, 57, pp. 58-100. 2022.

  
  

• Cluster Analysis of Trajectories Based on Segmend Splitting (CATBOSS)

  Paper: J. Damjanovic, J.M. Murphy, and Y.-S. Lin. "CATBOSS: Cluster Analysis of Trajectories Based on Segment Splitting". Journal of Chemical Information and Modeling, 61(1), pp. 5066-5081. 2021.




• Spatially Regularized Ultrametric Spectral Clustering (SRUSC).

  Paper: S. Zhang and J.M. Murphy. "Hyperspectral Image Clustering with Spatially-Regularized Ultrametrics". Remote Sensing, 13(5), 955. 2021.




• Global and Local Integrated Diffused Embedding (GLIDE)

  Paper: K. Devkota, J.M. Murphy, and L. Cowen. "GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks". Bioinformatics, 36(Issue Supplement_1), pp. i464-i473. 2020.




• Spatially Regularized Learning by Active Nonlinear Diffusion (SR-LAND)

  Paper: J.M. Murphy, "Spatially Regularized Active Diffusion Learning for High-Dimensional Images", Pattern Recognition Letters, Volume 135, pp. 213-220. 2020.




• Longest Leg Path Distance Spectral Clustering.

  Paper: A. Little, M. Maggioni, and J.M. Murphy. "Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms". Journal of Machine Learning Research, 21(6), pp. 1-66. 2020.




• Learning by Active Nonlinear Diffusion (LAND)

  Paper: M. Maggioni and J.M. Murphy, "Learning by Active Nonlinear Diffusion". Foundations of Data Science, 1(3), pp. 271-291. 2019.




• Diffusion Learning Toolbox, Fusion Supplement, Data.

  Papers:

  • J.M. Murphy and M. Maggioni. "Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion". IEEE Transactions on Geoscience and Remote Sensing, 57(3), pp. 1829-1845. 2019.
  • M. Maggioni, and J.M. Murphy. "Learning by Unsupervised Nonlinear Diffusion". Journal of Machine Learning Research, 20(160), pp. 1-56. 2019.
  • J.M. Murphy and M. Maggioni. "Spectral-Spatial Diffusion Geometry for Hyperspectral Image Clustering". IEEE Geoscience and Remote Sensing Letters. 17(7), pp. 1243-1247. 2020.
  • J.M. Murphy and M. Maggioni. "Diffusion Geometric Methods for Fusion of Remotely Sensed Data". Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV (Vol. 10644, p. 106440I). International Society for Optics and Photonics. 2018.
  
  

• Discrete Directional Gabor Frames

  Paper: W. Czaja, B. Manning, J.M. Murphy, and K. Stubbs. "Discrete Directional Gabor Frames". Applied and Computational Harmonic Analysis, 45(1), pp. 1-21. 2018.