Boosting with spatial regularization

Zhen James Xiang, Yongxin Taylor Xi, Uri Hasson, Peter Jeffrey Ramadge

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

22 Scopus citations

Abstract

By adding a spatial regularization kernel to a standard loss function formulation of the boosting problem, we develop a framework for spatially informed boosting. From this regularized loss framework we derive an efficient boosting algorithm that uses additional weights/priors on the base classifiers. We prove that the proposed algorithm exhibits a "grouping effect", which encourages the selection of all spatially local, discriminative base classifiers. The algorithm's primary advantage is in applications where the trained classifier is used to identify the spatial pattern of discriminative information, e.g. the voxel selection problem in fMRI. We demonstrate the algorithm's performance on various data sets.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference
Pages2107-2115
Number of pages9
StatePublished - Dec 1 2009
Event23rd Annual Conference on Neural Information Processing Systems, NIPS 2009 - Vancouver, BC, Canada
Duration: Dec 7 2009Dec 10 2009

Publication series

NameAdvances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Other

Other23rd Annual Conference on Neural Information Processing Systems, NIPS 2009
CountryCanada
CityVancouver, BC
Period12/7/0912/10/09

All Science Journal Classification (ASJC) codes

  • Information Systems

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  • Cite this

    Xiang, Z. J., Xi, Y. T., Hasson, U., & Ramadge, P. J. (2009). Boosting with spatial regularization. In Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference (pp. 2107-2115). (Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference).