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.
Geometrically Regularized Wasserstein Dictionary Learning (GeoWDL).
Paper: M. Mueller, S. Aeron, J.M. Murphy, A. Tasissa. "Geometrically Regularized Wasserstein Dictionary Learning". Topological, Algebraic and Geometric Learning Workshops, PMLR. 2023.
Paper: 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.
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.
Barycentric Coding Model (BCM).
Paper: M. Werenski, R. Jiang, A. Tasissa, S. Aeron, 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:
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.