TY - GEN

T1 - Blockwise coordinate descent procedures for the multi-task Lasso, with applications to neural semantic basis discovery

AU - Liu, Han

AU - Palatucci, Mark

AU - Zhang, Jian

PY - 2009/12/9

Y1 - 2009/12/9

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=71149111015&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=71149111015&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:71149111015

SN - 9781605585161

T3 - Proceedings of the 26th International Conference On Machine Learning, ICML 2009

SP - 649

EP - 656

BT - Proceedings of the 26th International Conference On Machine Learning, ICML 2009

T2 - 26th International Conference On Machine Learning, ICML 2009

Y2 - 14 June 2009 through 18 June 2009

ER -