Convex Optimization Learning of Faithful Euclidean Distance Representations in Nonlinear Dimensionality Reduction

22 Jun 2014Chao DingHou-Duo Qi

Classical multidimensional scaling only works well when the noisy distances observed in a high dimensional space can be faithfully represented by Euclidean distances in a low dimensional space. Advanced models such as Maximum Variance Unfolding (MVU) and Minimum Volume Embedding (MVE) use Semi-Definite Programming (SDP) to reconstruct such faithful representations... (read more)

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