15 citations · 34 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
Privacy Preserving Analytics on Distributed Medical Data
Marina Blanton, Ah Reum Kang, Subhadeep Karan +1
Objective: To enable privacy-preserving learning of high quality generative and discriminative machine learning models from distributed electronic health records. Methods and Resul…
Fast Counting in Machine Learning Applications
Subhadeep Karan, Matthew Eichhorn, Blake Hurlburt +2
We propose scalable methods to execute counting queries in machine learning applications. To achieve memory and computational efficiency, we abstract counting queries and their con…
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…