1 citations · 1 across the 2 of their papers we have counts for
7 papers
Hyperspectral Image Clustering with Spatially-Regularized Ultrametrics
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. The proposed method…
Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks
Lenore Cowen, Kapil Devkota, Xiaozhe Hu +2
Data-dependent metrics are powerful tools for learning the underlying structure of high-dimensional data. This article develops and analyzes a data-dependent metric known as diffus…
Spatially regularized active diffusion learning for high-dimensional images
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 clust…
Learning by Active Nonlinear Diffusion
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. Diffusion distances are…
Spectral-Spatial Diffusion Geometry for Hyperspectral Image Clustering
James M. Murphy, Mauro Maggioni
An unsupervised learning algorithm to cluster hyperspectral image (HSI) data is proposed that exploits spatially-regularized random walks. Markov diffusions are defined on the spac…
Spectral Analysis for Non-Hermitian Matrices and Directed Graphs
Edinah K. Gnang, James M. Murphy
We generalize classical results in spectral graph theory and linear algebra more broadly, from the case where the underlying matrix is Hermitian to the case where it is non-Hermiti…