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Found 3 papers, 1 papers with code
Scalable Manifold Learning for Big Data with Apache Spark
Non-linear spectral dimensionality reduction methods, such as Isomap, remain important technique for learning manifolds.
Entropy-Isomap: Manifold Learning for High-dimensional Dynamic Processes
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.
Error Metrics for Learning Reliable Manifolds from Streaming Data
In this paper, we argue that a stable manifold can be learned using only a fraction of the stream, and the remaining stream can be mapped to the manifold in a significantly less costly manner.
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