SVD using netlib-java for PCA

h2oai / h2o-3

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Apache License 2.0

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H2O provides implementation of the PCA algorithm which depends on the Jama library. The library is used for several tasks including Singular Value Decomposition (SVD). However, the library also introduces sub-optimal performance.

The idea is to replace Jama SVD computation by the netlib-java library [MTJ|https://github.com/fommil/matrix-toolkits-java], and measure performance impact.

This task is based on assignment [Improve H2O PCA|https://gist.github.com/mmalohlava/717ad7b7441a6ff91b5f0a907482bd5d]

h2oai / h2o-3

SVD using netlib-java for PCA #11264