Minimum Volume Embedding

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A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, neutral): “(5) + O(SVM classifier) HSCA [57] PMI O(MMDW) + (1st order proximity constraint) MVE [107] KNN,W ¼ argmin trðWð Pd i¼1 yiy T i þ PN i¼dþ1 yiy T i ÞÞ Eq.”
Sparse Local Embeddings for Extreme Multi-label Classification (2015, background): “Note that the above problem formulation is somewhat similar to a few existing methods for nonlinear dimensionality reduction that also seek to preserve distances to a few near neighbors [18, 19].”
Structure preserving embedding (2009, methodology): “Similarly, we present an adaptation of the MVE algorithm called MVE+SP that adds the kNN structure preserving constraints to the MVE semidefinite program ( Shaw & Jebara, 2007 ).”
Sparse Manifold Clustering and Embedding (2011, methodology): “Global methods, such as Isomap [4], Semidefinite embedding [5], Minimum volume embedding [6] and Structure preserving embedding [7], try to preserve local and global relationships among all data points.”