Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a kernel matrix for nonlinear dimensionality reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A kernel view of the dimensionality reduction of manifolds
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Head Pose Estimation by Nonlinear Manifold Learning
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
SLEPc: A scalable and flexible toolkit for the solution of eigenvalue problems
ACM Transactions on Mathematical Software (TOMS) - Special issue on the Advanced CompuTational Software (ACTS) Collection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
The Journal of Machine Learning Research
Pattern Recognition
Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
Improved Nyström low-rank approximation and error analysis
Proceedings of the 25th international conference on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Higher Dimensional Affine Registration and Vision Applications
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Semi-supervised graph clustering: a kernel approach
Machine Learning
Pattern Recognition
Visual tracking and recognition using probabilistic appearance manifolds
Computer Vision and Image Understanding
Planar arrangement of high-dimensional biomedical data sets by isomap coordinates
CBMS'03 Proceedings of the 16th IEEE conference on Computer-based medical systems
Fast density-weighted low-rank approximation spectral clustering
Data Mining and Knowledge Discovery
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Nonlinear Discriminant Analysis on Embedded Manifold
IEEE Transactions on Circuits and Systems for Video Technology
Graph dual regularization non-negative matrix factorization for co-clustering
Pattern Recognition
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In this paper, we derive an ensemble method inspired by boosting, a novel Robust Positive semidefinite L-Isomap Ensemble (RPL-IsomapE) approach. Specifically, we first apply a constant-shifting method to yield a symmetric positive semidefinite (SPSD) matrix. For topological stability, we also employ a method for eliminating critical outlier points using the confusion rate of all the data points. Then we align individual Robust Positive semidefinite L-Isomap (RPL-Isomap) solutions in common coordinate system through high dimensional affine transformations. Finally, we combine multiple RPL-Isomap solutions by the weighted averaging procedure according to residual variance to improve the noise-robustness of our method. Our RPL-IsomapE maintains the scalability and the speed of L-Isomap. Experiments on two images data sets and a video data set confirm the promising performance of the proposed RPL-IsomapE.