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https://issues.apache.org/jira/browse/SPARK-1585?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xusen Yin closed SPARK-1585.
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Resolution: Fixed
Parameter tuning is vital for LASSO, especially the step size. Large step size
causes large updating value, then infinity occurs.
> Not robust Lasso causes Infinity on weights and losses
> ------------------------------------------------------
>
> Key: SPARK-1585
> URL: https://issues.apache.org/jira/browse/SPARK-1585
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 0.9.1
> Reporter: Xusen Yin
> Assignee: Xusen Yin
> Fix For: 1.1.0
>
>
> Lasso uses LeastSquaresGradient and L1Updater, but
> diff = brzWeights.dot(brzData) - label
> in LeastSquaresGradient would cause too big diff, then will affect the
> L1Updater, which increases weights exponentially. Small shrinkage value
> cannot lasso weights back to zero then. Finally, the weights and losses reach
> Infinity.
> For example, data = (0.5 repeats 10k times), weights = (0.6 repeats 10k
> times), then data.dot(weights) approximates 300+, the diff will be 300. Then
> L1Updater sets weights to approximate 300. In the next iteration, the weights
> will be set to approximate 30000, and so on.
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