Feature-based correspondence: an eigenvector approach
Image and Vision Computing - Special issue: BMVC 1991
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
A Multibody Factorization Method for Independently Moving Objects
International Journal of Computer Vision
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Combinatorial Theory Series A
Graph Drawing by High-Dimensional Embedding
GD '02 Revised Papers from the 10th International Symposium on Graph Drawing
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
A multi-body factorization method for motion analysis
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
An Eigenspace Projection Clustering Method for Inexact Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Matching using Spectral Embedding and Alignment
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Recognising Facial Expressions Using Spherical Harmonics
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Commute times of random walks on trees
Discrete Applied Mathematics
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This paper explores the use of commute-time preserving embedding as means of data-clustering. Commute time is a measure of the time taken for a random walk to set-out and return between a pair of nodes on a graph. It may be computed from the spectrum of the Laplacian matrix. Since the commute time is averaged over all potential paths between a pair of nodes, it is potentially robust to variations in graph structure due to edge insertions or deletions. Here we demonstrate how nodes of a graph can be embedded in a vector space in a manner that preserves commute time. We present a number of important properties of the embedding. We experiment with the method for separating object motions in image sequences.