Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
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
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
A kernel view of the dimensionality reduction of manifolds
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A circuit framework for robust manifold learning
Neurocomputing
Human emotion and the uncanny valley: a GLM, MDS, and Isomap analysis of robot video ratings
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Person-independent head pose estimation using biased manifold embedding
EURASIP Journal on Advances in Signal Processing
Weighted Kernel Isomap for Data Visualization and Pattern Classification
Computational Intelligence and Security
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Local relative transformation with application to isometric embedding
Pattern Recognition Letters
Gesture Recognition Based on Manifold Learning
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Manifold integration with Markov random walks
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Sketch recognition based on manifold learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Fast and accurate retinal vasculature tracing and kernel-Isomap-based feature selection
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Nonlinear Dimensionality Reduction by Topologically Constrained Isometric Embedding
International Journal of Computer Vision
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Robust Positive semidefinite L-Isomap Ensemble
Pattern Recognition Letters
Operators for transforming kernels into quasi-local kernels that improve SVM accuracy
Journal of Intelligent Information Systems
Neurocomputing
Improved locally linear embedding by cognitive geometry
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Learning orthogonal projections for Isomap
Neurocomputing
Identification of moving vehicle trajectory using manifold learning
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
A kernel-based framework for image collection exploration
Journal of Visual Languages and Computing
Data visualization for asymmetric relations
Neurocomputing
Regularized discriminant entropy analysis
Pattern Recognition
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Isomap is one of widely used low-dimensional embedding methods, where geodesic distances on a weighted graph are incorporated with the classical scaling (metric multidimensional scaling). In this paper we pay our attention to two critical issues that were not considered in Isomap, such as: (1) generalization property (projection property); (2) topological stability. Then we present a robust kernel Isomap method, armed with such two properties. We present a method which relates the Isomap to Mercer kernel machines, so that the generalization property naturally emerges, through kernel principal component analysis. For topological stability, we investigate the network flow in a graph, providing a method for eliminating critical outliers. The useful behavior of the robust kernel Isomap is confirmed through numerical experiments with several data sets.