Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Direction Divisive Partitioning
Data Mining and Knowledge Discovery
Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Analysis and extension of spectral methods for nonlinear dimensionality reduction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local multidimensional scaling
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection
The Journal of Machine Learning Research
Terrorist organization behavior prediction algorithm based on context subspace
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Distributed spectral cluster management: a method for building dynamic publish/subscribe systems
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
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In the past decade, a number of nonlinear dimensionality reduction methods using an affinity graph have been developed for manifold learning. This paper explores a multilevel framework with the goal of reducing the cost of unsupervised manifold learning and preserving the embedding quality at the same time. An application to spectral clustering is also presented. Experimental results indicate that our multilevel approach is an appealing alternative to standard techniques.