Search Results for author: James M. Murphy

Found 20 papers, 9 papers with code

On Rank Energy Statistics via Optimal Transport: Continuity, Convergence, and Change Point Detection

no code implementations15 Feb 2023 Matthew Werenski, Shoaib Bin Masud, James M. Murphy, Shuchin Aeron

This paper considers the use of recently proposed optimal transport-based multivariate test statistics, namely rank energy and its variant the soft rank energy derived from entropically regularized optimal transport, for the unsupervised nonparametric change point detection (CPD) problem.

Change Point Detection

Geometric Sparse Coding in Wasserstein Space

no code implementations21 Oct 2022 Marshall Mueller, Shuchin Aeron, James M. Murphy, Abiy Tasissa

We show this approach leads to sparse representations in Wasserstein space and addresses the problem of non-uniqueness of barycentric representation.

Dictionary Learning

Unsupervised Spatial-spectral Hyperspectral Image Reconstruction and Clustering with Diffusion Geometry

1 code implementation28 Apr 2022 Kangning Cui, Ruoning Li, Sam L. Polk, James M. Murphy, Robert J. Plemmons, Raymond H. Chan

DSIRC then locates high-density, high-purity pixels far in diffusion distance (a data-dependent distance metric) from other high-density, high-purity pixels and treats these as cluster exemplars, giving each a unique label.

Image Reconstruction

Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral Images

1 code implementation13 Apr 2022 Sam L. Polk, Kangning Cui, Robert J. Plemmons, James M. Murphy

Hyperspectral images encode rich structure that can be exploited for material discrimination by machine learning algorithms.

Active Learning Image Segmentation +1

Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images

1 code implementation18 Mar 2022 Sam L. Polk, Kangning Cui, Aland H. Y. Chan, David A. Coomes, Robert J. Plemmons, James M. Murphy

Hyperspectral images taken from aircraft or satellites contain information from hundreds of spectral bands, within which lie latent lower-dimensional structures that can be exploited for classifying vegetation and other materials.

Image Clustering

Measure Estimation in the Barycentric Coding Model

1 code implementation28 Jan 2022 Matthew Werenski, Ruijie Jiang, Abiy Tasissa, Shuchin Aeron, James M. Murphy

Our first main result leverages the Riemannian geometry of Wasserstein-2 space to provide a procedure for recovering the barycentric coordinates as the solution to a quadratic optimization problem assuming access to the true reference measures.

Multivariate rank via entropic optimal transport: sample efficiency and generative modeling

no code implementations29 Oct 2021 Shoaib Bin Masud, Matthew Werenski, James M. Murphy, Shuchin Aeron

We leverage this result to demonstrate fast convergence of sample sRE and sRMMD to their population version making them useful for high-dimensional GoF testing.

feature selection Image Generation +1

A Multiscale Environment for Learning by Diffusion

1 code implementation31 Jan 2021 James M. Murphy, Sam L. Polk

To efficiently learn the multiscale structure observed in many real datasets, we introduce the Multiscale Learning by Unsupervised Nonlinear Diffusion (M-LUND) clustering algorithm, which is derived from a diffusion process at a range of temporal scales.

K-Deep Simplex: Deep Manifold Learning via Local Dictionaries

1 code implementation3 Dec 2020 Pranay Tankala, Abiy Tasissa, James M. Murphy, Demba Ba

We theoretically analyze the proposed program by relating the weighted $\ell_1$ penalty in KDS to a weighted $\ell_0$ program.

Deep Clustering Dictionary Learning +1

Hyperspectral Image Clustering with Spatially-Regularized Ultrametrics

1 code implementation10 Apr 2020 Shukun Zhang, James M. Murphy

We propose a method for the unsupervised clustering of hyperspectral images based on spatially regularized spectral clustering with ultrametric path distances.

Image Clustering

Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks

no code implementations7 Mar 2020 Lenore Cowen, Kapil Devkota, Xiaozhe Hu, James M. Murphy, Kaiyi Wu

This article develops a theory for DSD based on the multitemporal emergence of mesoscopic equilibria in the underlying diffusion process.

Denoising Dimensionality Reduction

Spatially regularized active diffusion learning for high-dimensional images

no code implementations6 Nov 2019 James M. Murphy

An active learning algorithm for the classification of high-dimensional images is proposed in which spatially-regularized nonlinear diffusion geometry is used to characterize cluster cores.

Active Learning Vocal Bursts Intensity Prediction

Learning by Active Nonlinear Diffusion

no code implementations30 May 2019 Mauro Maggioni, James M. Murphy

This article proposes an active learning method for high dimensional data, based on intrinsic data geometries learned through diffusion processes on graphs.

Active Learning

Spectral-Spatial Diffusion Geometry for Hyperspectral Image Clustering

no code implementations8 Feb 2019 James M. Murphy, Mauro Maggioni

The explicit incorporation of spatial regularity into the diffusion construction leads to smoother random processes that are more adapted for unsupervised machine learning than those based on spectra alone.

Density Estimation Image Clustering

Learning by Unsupervised Nonlinear Diffusion

no code implementations15 Oct 2018 Mauro Maggioni, James M. Murphy

This paper proposes and analyzes a novel clustering algorithm that combines graph-based diffusion geometry with techniques based on density and mode estimation.

Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms

1 code implementation17 Dec 2017 Anna Little, Mauro Maggioni, James M. Murphy

We consider the problem of clustering with the longest-leg path distance (LLPD) metric, which is informative for elongated and irregularly shaped clusters.

Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion

no code implementations26 Apr 2017 James M. Murphy, Mauro Maggioni

The problem of unsupervised learning and segmentation of hyperspectral images is a significant challenge in remote sensing.

Active Learning

Superresolution of Noisy Remotely Sensed Images Through Directional Representations

no code implementations27 Feb 2016 Wojciech Czaja, James M. Murphy, Daniel Weinberg

We justify the use of shearlets mathematically, before presenting a denoising single-image superresolution algorithm that combines the shearlet transform with sparse mixing estimators (SME).

Denoising Edge Detection +1

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