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Yanbo Liang edited comment on SPARK-3181 at 7/11/16 2:35 PM: ------------------------------------------------------------- [~dbtsai] [~MechCoder] There is one problem need to be discussed: The scaling factor {{\sigma}} has to be estimated as well, and in Eq.(6) {{\sigma}} has to be >= 0. So we can have two alternatives: * #1, Use {{\sigma}} directly and L-BFGS-B as the solver. The breeze library supports L-BFGS-B recently, so we need to bump up the dependent breeze version to 0.12 firstly. We can only support L2 regularization as what you did in scikit-learn. However, we can not support L1 or elasticNet regularization currently and in the near future, since there is no plan to add OWLQN-B to breeze as https://github.com/scalanlp/breeze/issues/455 said. * #2, Replace {{\sigma}} to {{\exp(\alpha)}}, then we can support elasticNet regularization like what we do in {{LinearRegression}}. But there is no proof that it will be jointly convex with {{\alpha}} for the huber loss function, we may fall into the local optimum. I prefer to opinion #1 which will give the exact solution and looking forward to hear your thoughts. If opinion #1 is ok for you, I will first open a ticket to test and bump up breeze version to 0.12. was (Author: yanboliang): [~dbtsai] [~MechCoder] There is one problem need to be discussed: The scaling factor {{\sigma}} has to be estimated as well, and in Eq.(6) {{\sigma}} has to be >= 0. So we can have two alternatives: * #1, Use {{\sigma}} directly and L-BFGS-B as the solver. The breeze library supports L-BFGS-B recently, so we need to bump up the dependent breeze version to 0.12 firstly. We can only support L2 regularization as what you did in scikit-learn. However, we can not support L1 or elasticNet regularization currently and in the near future, since there is no plan to add OWLQN-B to breeze as https://github.com/scalanlp/breeze/issues/455 said. * #2, Replace {{\sigma}} to {{\exp(\alpha)}}, then we can support elasticNet regularization like what we do in {{LinearRegression}}. But there is no proof that it will be jointly convex with {{\alpha}} for the huber loss function, we may fall into the local optimum. I prefer to opinion #1 which will give the exact solution and looking forward to hear your thoughts. > Add Robust Regression Algorithm with Huber Estimator > ---------------------------------------------------- > > Key: SPARK-3181 > URL: https://issues.apache.org/jira/browse/SPARK-3181 > Project: Spark > Issue Type: New Feature > Components: ML, MLlib > Reporter: Fan Jiang > Assignee: Yanbo Liang > Labels: features > Original Estimate: 0h > Remaining Estimate: 0h > > Linear least square estimates assume the error has normal distribution and > can behave badly when the errors are heavy-tailed. In practical we get > various types of data. We need to include Robust Regression to employ a > fitting criterion that is not as vulnerable as least square. > In 1973, Huber introduced M-estimation for regression which stands for > "maximum likelihood type". The method is resistant to outliers in the > response variable and has been widely used. > The new feature for MLlib will contain 3 new files > /main/scala/org/apache/spark/mllib/regression/RobustRegression.scala > /test/scala/org/apache/spark/mllib/regression/RobustRegressionSuite.scala > /main/scala/org/apache/spark/examples/mllib/HuberRobustRegression.scala > and one new class HuberRobustGradient in > /main/scala/org/apache/spark/mllib/optimization/Gradient.scala -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org