15 citations · 19 across the 3 of their papers we have counts for
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cs.DC2018
Scalable Manifold Learning for Big Data with Apache Spark
Frank Schoeneman, Jaroslaw Zola
Non-linear spectral dimensionality reduction methods, such as Isomap, remain important technique for learning manifolds. However, due to computational complexity, exact manifold le…
stat.ML2018
Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes
Frank Schoeneman, Varun Chandola, Nils Napp +2
Scientific and engineering processes deliver massive high-dimensional data sets that are generated as non-linear transformations of an initial state and few process parameters. Map…