Laplacian Eigenmaps for Dimensionality Reduction and Data Representation


One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data. We consider the problem of constructing a representation for data lying on a lowdimensional manifold embedded in a high-dimensional space. Drawing on the correspondence between the graph Laplacian, the Laplace Beltrami operator… (More)
DOI: 10.1162/089976603321780317
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