Regularization paths and coordinate descent

Conference Paper · January 2008with3 Reads
DOI: 10.1145/1401890.1401893 · Source: DBLP
Conference: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, August 24-27, 2008

    Abstract

    In a statistical world faced with an explosion of data, regularization has become an important ingredient. In a wide variety of problems we have many more input features than observations, and the lasso penalty and its hybrids have become increasingly useful for both feature selection and regularization. This talk presents some effective algorithms based on coordinate descent for fitting large scale regularization paths for a variety of problems.