Manifold learning
From Wikipedia, the free encyclopedia
Manifold learning is a technique used to reduce dimensionality of data. It differs from other dimension reduction techniques, such as principal component analysis (PCA), in that it can support highly nonlinear subspaces.
[edit] External links
- Introduction - http://www.nlpr.ia.ac.cn/users/szli/papers/ZHP-MLA-Chapter.pdf
- http://www.cse.msu.edu/~lawhiu/manifold/
- http://www.iipl.fudan.edu.cn/~zhangjp/literatures/MLF/INDEX.HTM
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