Semidefinite Embedding
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High Quality Content by WIKIPEDIA articles! Semidefinite embedding (SDE) or maximum variance unfolding (MVU) is an algorithm in computer science, that uses semidefinite programming to perform non-linear dimensionality reduction of high-dimensional vectorial input data. Non-linear dimensionality reduction algorithms attempt to map high-dimensional data onto a low-dimensional Euclidean vector space. Maximum variance Unfolding is a member of the manifold learning family, which also include algorithms such as isomap and locally linear embedding. In manifold learning, the input data is assumed to b...
High Quality Content by WIKIPEDIA articles! Semidefinite embedding (SDE) or maximum variance unfolding (MVU) is an algorithm in computer science, that uses semidefinite programming to perform non-linear dimensionality reduction of high-dimensional vectorial input data. Non-linear dimensionality reduction algorithms attempt to map high-dimensional data onto a low-dimensional Euclidean vector space. Maximum variance Unfolding is a member of the manifold learning family, which also include algorithms such as isomap and locally linear embedding. In manifold learning, the input data is assumed to be sampled from a low dimensional manifold that is embedded inside of a higher dimensional vector space. The main intuition behind MVU is to exploit the local linearity of manifolds and create a mapping that preserves local neighborhoods at every point of the underlying manifold.
