Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Learning Eigenfunctions Links Spectral Embedding and Kernel PCA
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Editorial: Hybrid learning machines
Neurocomputing
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Diffusion Maps is a new powerful technique for dimensionality reduction that can capture geometric structure while taking into account data distribution. In this work we will apply it to time and spatial compression of numerical weather forecasts, showing how it is capable to greatly reduce the initial dimension while still capturing relevant information in the original data.