Computing communities in large networks using random walks
Dense subgraphs of sparse graphs (communities), which appear in most real-world complex
networks, play an important role in many contexts. Computing them however is generally …
networks, play an important role in many contexts. Computing them however is generally …
Random walks for image segmentation
L Grady - IEEE transactions on pattern analysis and machine …, 2006 - ieeexplore.ieee.org
A novel method is proposed for performing multilabel, interactive image segmentation.
Given a small number of pixels with user-defined (or predefined) labels, one can analytically …
Given a small number of pixels with user-defined (or predefined) labels, one can analytically …
[图书][B] Nonlinear dimensionality reduction
JA Lee, M Verleysen - 2007 - Springer
Methods of dimensionality reduction provide a way to understand and visualize the structure
of complex data sets. Traditional methods like principal component analysis and classical …
of complex data sets. Traditional methods like principal component analysis and classical …
Computing communities in large networks using random walks
Dense subgraphs of sparse graphs (communities), which appear in most real-world complex
networks, play an important role in many contexts. Computing them however is generally …
networks, play an important role in many contexts. Computing them however is generally …
Isolation by resistance
BH McRae - Evolution, 2006 - academic.oup.com
Despite growing interest in the effects of landscape heterogeneity on genetic structuring, few
tools are available to incorporate data on landscape composition into population genetic …
tools are available to incorporate data on landscape composition into population genetic …
Diffusion maps, spectral clustering and reaction coordinates of dynamical systems
A central problem in data analysis is the low dimensional representation of high dimensional
data and the concise description of its underlying geometry and density. In the analysis of …
data and the concise description of its underlying geometry and density. In the analysis of …
[图书][B] Discrete calculus: Applied analysis on graphs for computational science
LJ Grady, JR Polimeni - 2010 - Springer
The field of discrete calculus, also known as" discrete exterior calculus", focuses on finding a
proper set of definitions and differential operators that make it possible to operate the …
proper set of definitions and differential operators that make it possible to operate the …
Sub-Markov random walk for image segmentation
A novel sub-Markov random walk (subRW) algorithm with label prior is proposed for seeded
image segmentation, which can be interpreted as a traditional random walker on a graph …
image segmentation, which can be interpreted as a traditional random walker on a graph …
[PDF][PDF] Recent Advances in Nonlinear Dimensionality Reduction, Manifold and Topological Learning.
The ever-growing amount of data stored in digital databases raises the question of how to
organize and extract useful knowledge. This paper outlines some current developments in …
organize and extract useful knowledge. This paper outlines some current developments in …
[PDF][PDF] Hitting and commute times in large random neighborhood graphs
U Von Luxburg, A Radl, M Hein - The Journal of Machine Learning …, 2014 - jmlr.org
In machine learning, a popular tool to analyze the structure of graphs is the hitting time and
the commute distance (resistance distance). For two vertices u and v, the hitting time Huv is …
the commute distance (resistance distance). For two vertices u and v, the hitting time Huv is …