Manifold learning for visualizing and analyzing high-dimensional data
J Zhang, H Huang, J Wang - IEEE Intelligent Systems, 2010 - computer.org
J Zhang, H Huang, J Wang
IEEE Intelligent Systems, 2010•computer.org… This article describes a few representative manifold-learning algorithms and
demonstrates their utility in visualizing and analyzing high-dimensional data. We also
discuss their strengths and weaknesses, along with strategies to avoid pitfalls. … Therefore,
a manifold is a generalization of Euclidean space, but not a special one. Assuming that data
reside in a low-dimensional manifold that is then embedded within a high-dimensional
space, the goal of manifold learning is to uncover such low-dimensional manifolds from a …
demonstrates their utility in visualizing and analyzing high-dimensional data. We also
discuss their strengths and weaknesses, along with strategies to avoid pitfalls. … Therefore,
a manifold is a generalization of Euclidean space, but not a special one. Assuming that data
reside in a low-dimensional manifold that is then embedded within a high-dimensional
space, the goal of manifold learning is to uncover such low-dimensional manifolds from a …
Abstract
Assuming that high-dimensional data are generated from intrinsic variables with lower dimensions, several key manifold-learning algorithms can help effectively analyze and visualize such data.
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