15 citations · 19 across the 3 of their papers we have counts for
4 papers
Solving All-Pairs Shortest-Paths Problem in Large Graphs Using Apache Spark
Frank Schoeneman, Jaroslaw Zola
Algorithms for computing All-Pairs Shortest-Paths (APSP) are critical building blocks underlying many practical applications. The standard sequential algorithms, such as Floyd-Wars…
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…
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…
Error Metrics for Learning Reliable Manifolds from Streaming Data
Frank Schoeneman, Suchismit Mahapatra, Varun Chandola +2
Spectral dimensionality reduction is frequently used to identify low-dimensional structure in high-dimensional data. However, learning manifolds, especially from the streaming data…