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Nonlinear Representation and Dimensionality Reduction
2023Hye Sun Yun +3 more
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Nonlinear Dimensionality Reduction and Manifold Learning
2013We have little visual guidance to help us identify any meaningful lowdimensional structure hidden in high-dimensional data. The linear projection methods of Chapter 7 can be extremely useful in discovering lowdimensional structure when the data actually lie in a linear (or approximately linear) lower-dimensional subspace (called a manifold) ℳ of input ...
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Nonlinear Dimensionality Reduction for Data with Disconnected Neighborhood Graph
Neural Processing Letters, 2017Jicong Fan, T. Chow, Mingbo Zhao, J. Ho
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Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Neural Computation, 2003M. Belkin, P. Niyogi
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Nonlinear Methods for Dimensionality Reduction
2013Charles K. Chui, Jianzhong Wang
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