Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Robust locally linear embedding
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Pattern Analysis & Applications - Special Issue: Non-parametric distance-based classification techniques and their applications
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PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Efficient Parallel Algorithm for Nonlinear Dimensionality Reduction on GPU
GRC '10 Proceedings of the 2010 IEEE International Conference on Granular Computing
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LLE(Local linear embedding) and Isomap are widely used approaches for dimension reduction on data complex. The embedding results from the two methods are eigenvectors from solving specific matrices. The corresponding eigenvalues for the selected eigenvectors have important meaning for the embedding results. In this paper, the k-nn method and ε-distance approach are used for neighborhood function with parameters. Then, different datasets and parameters will be applied to obtain the embedding results and eigenvalues. The main change of eigenvalues and the corresponding embedding results will be shown in this paper.