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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 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.