Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
نویسندگان
چکیده
منابع مشابه
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 on the manifold, and the connections ...
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For a given set of data points lying on a low-dimensional manifold embedded in a high-dimensional space, the dimensionality reduction is to recover a low-dimensional parametrization from the data set. The recently developed Hessian Eigenmaps is a mathematically rigorous method that also sets a theoretical framework for the nonlinear dimensionality reduction problem. In this paper, we develop a ...
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ژورنال
عنوان ژورنال: Neural Computation
سال: 2003
ISSN: 0899-7667,1530-888X
DOI: 10.1162/089976603321780317