Principal Surfaces from Unsupervised Kernel Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Image Manifolds by Semidefinite Programming
International Journal of Computer Vision
Semi-supervised nonlinear dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Discriminant Projection Analysis for Feature Extr
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Data Fusion and Multicue Data Matching by Diffusion Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-definite Manifold Alignment
ECML '07 Proceedings of the 18th European conference on Machine Learning
Clustering with local and global regularization
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Semi-supervised classification using local and global regularization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
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Local structures and global structures of data sets are both important information for learning from highly nonlinear data. However, existing manifold learning algorithms either neglect one of them or have limitation on describing them. In this paper, we proposed a new two-step framework that fusing the global and local information to unfold highly nonlinear data. It first learns the global structures via a new method-Distance Penalization Embedding and then refines the local structures by semi-supervised manifold learning algorithms. The effectiveness of the method has been verified by experimental results on both simulation and real world data sets.