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Robust Non-linear Dimensionality Reduction using Successive 1-Dimensional Laplacian Eigenmapse
Samuel Gerber
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Oznake: computer science;machine learning;preprocessing;manifold learning
Non-linear dimensionality reduction of noisy data is a challenging problem encountered in a variety of data analysis applications. Recent results in the literature show that spectral decomposition, as used for example by the Laplacian Eigenmaps algorithm, provides a powerful tool for non-linear dime ...
Leto: 2007 Vir: videolectures.net
Št. zadetkov: 1
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