Nonlinear Dimensionality Reduction with Locally Linear Embedding and Isomap
نویسندگان
چکیده
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منابع مشابه
Nonlinear Dimensionality Reduction – Locally Linear Embedding versus Isomap
Real data of natural and social sciences is often very high-dimensional. However, the underlying structure can in many cases be described by a small number of features. Recently two new nonlinear methods for reducing the dimensionality of such data, Locally Linear Embedding and Isomap, have been suggested and successfully applied. This report compares both algorithms by means of several synthet...
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