Blockwise coordinate descent procedures for the multi-task Lasso, with applications to neural semantic basis discovery

Han Liu, Mark Palatucci, Jian Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

119 Scopus citations

Abstract

We develop a cyclical blockwise coordinate descent algorithm for the multi-task Lasso that efficiently solves problems with thousands of features and tasks. The main result shows that a closed-form Winsorization operator can be obtained for the sup-norm penalized least squares regression. This allows the algorithm to find solutions to very large-scale problems far more efficiently than existing methods. This result complements the pioneering work of Friedman, et al. (2007) for the single-task Lasso. As a case study, we use the multi-task Lasso as a variable selector to discover a semantic basis for predicting human neural activation. The learned solution outperforms the standard basis for this task on the majority of test participants, while requiring far fewer assumptions about cognitive neuroscience. We demonstrate how this learned basis can yield insights into how the brain represents the meanings of words.

Original languageEnglish (US)
Title of host publicationProceedings of the 26th International Conference On Machine Learning, ICML 2009
Pages649-656
Number of pages8
StatePublished - Dec 9 2009
Event26th International Conference On Machine Learning, ICML 2009 - Montreal, QC, Canada
Duration: Jun 14 2009Jun 18 2009

Publication series

NameProceedings of the 26th International Conference On Machine Learning, ICML 2009

Other

Other26th International Conference On Machine Learning, ICML 2009
CountryCanada
CityMontreal, QC
Period6/14/096/18/09

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software

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    Liu, H., Palatucci, M., & Zhang, J. (2009). Blockwise coordinate descent procedures for the multi-task Lasso, with applications to neural semantic basis discovery. In Proceedings of the 26th International Conference On Machine Learning, ICML 2009 (pp. 649-656). (Proceedings of the 26th International Conference On Machine Learning, ICML 2009).