Seth Hendrickson created SPARK-17748:
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Summary: One-pass algorithm for linear regression with L1 and
elastic-net penalties
Key: SPARK-17748
URL: https://issues.apache.org/jira/browse/SPARK-17748
Project: Spark
Issue Type: Bug
Components: ML
Reporter: Seth Hendrickson
Currently linear regression uses weighted least squares to solve the normal
equations locally on the driver when the dimensionality is small (<4096).
Weighted least squares uses a Cholesky decomposition to solve the problem with
L2 regularization (which has a closed-form solution). We can support
L1/elasticnet penalties by solving the equations locally using OWL-QN solver.
Also note that Cholesky does not handle singular covariance matrices, but
L-BFGS and OWL-QN are capable of providing reasonable solutions. This patch can
also add support for solving singular covariance matrices by also adding L-BFGS.
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