Perturbation Bounds for Procrustes, Classical Scaling, and Trilateration, with Applications to Manifold Learning

22 Oct 2018Ery Arias-CastroAdel JavanmardBruno Pelletier

One of the common tasks in unsupervised learning is dimensionality reduction, where the goal is to find meaningful low-dimensional structures hidden in high-dimensional data. Sometimes referred to as manifold learning, this problem is closely related to the problem of localization, which aims at embedding a weighted graph into a low-dimensional Euclidean space... (read more)

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