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Reference.org
Nonlinear dimensionality reduction
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<p>Nonlinear dimensionality reduction, also known as manifold learning, is any of various related techniques that aim to project high-dimensional data, potentially existing across non-linear manifolds which cannot be adequately captured by linear decomposition methods, onto lower-dimensional <a href="/facts/Latent_manifold/IDYfRCU7">latent manifolds</a>, with the goal of either visualizing the data in the low-dimensional space, or learning the mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa) itself. The techniques described below can be understood as generalizations of linear decomposition methods used for <a href="/facts/Dimensionality_reduction/XoRpcrB0">dimensionality reduction</a>, such as <a href="/facts/Singular_value_decomposition/8t3v6wk3">singular value decomposition</a> and <a href="/facts/Principal_component_analysis/pCbVTqdR">principal component analysis</a>. </p>