Representational Similarity Learning with Application to Brain Networks

Urvashi Oswal, Christopher Cox, Matthew Lambon-Ralph, Timothy Rogers, Robert Nowak
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1041-1049, 2016.

Abstract

Representational Similarity Learning (RSL) aims to discover features that are important in representing (human-judged) similarities among objects. RSL can be posed as a sparsity-regularized multi-task regression problem. Standard methods, like group lasso, may not select important features if they are strongly correlated with others. To address this shortcoming we present a new regularizer for multitask regression called Group Ordered Weighted \ell_1 (GrOWL). Another key contribution of our paper is a novel application to fMRI brain imaging. Representational Similarity Analysis (RSA) is a tool for testing whether localized brain regions encode perceptual similarities. Using GrOWL, we propose a new approach called Network RSA that can discover arbitrarily structured brain networks (possibly widely distributed and non-local) that encode similarity information. We show, in theory and fMRI experiments, how GrOWL deals with strongly correlated covariates.

Cite this Paper

BibTeX
@InProceedings{pmlr-v48-oswal16, title = {Representational Similarity Learning with Application to Brain Networks}, author = {Oswal, Urvashi and Cox, Christopher and Lambon-Ralph, Matthew and Rogers, Timothy and Nowak, Robert}, booktitle = {Proceedings of The 33rd International Conference on Machine Learning}, pages = {1041--1049}, year = {2016}, editor = {Balcan, Maria Florina and Weinberger, Kilian Q.}, volume = {48}, series = {Proceedings of Machine Learning Research}, address = {New York, New York, USA}, month = {20--22 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v48/oswal16.pdf}, url = {https://proceedings.mlr.press/v48/oswal16.html}, abstract = {Representational Similarity Learning (RSL) aims to discover features that are important in representing (human-judged) similarities among objects. RSL can be posed as a sparsity-regularized multi-task regression problem. Standard methods, like group lasso, may not select important features if they are strongly correlated with others. To address this shortcoming we present a new regularizer for multitask regression called Group Ordered Weighted \ell_1 (GrOWL). Another key contribution of our paper is a novel application to fMRI brain imaging. Representational Similarity Analysis (RSA) is a tool for testing whether localized brain regions encode perceptual similarities. Using GrOWL, we propose a new approach called Network RSA that can discover arbitrarily structured brain networks (possibly widely distributed and non-local) that encode similarity information. We show, in theory and fMRI experiments, how GrOWL deals with strongly correlated covariates.} }
Endnote
%0 Conference Paper %T Representational Similarity Learning with Application to Brain Networks %A Urvashi Oswal %A Christopher Cox %A Matthew Lambon-Ralph %A Timothy Rogers %A Robert Nowak %B Proceedings of The 33rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Maria Florina Balcan %E Kilian Q. Weinberger %F pmlr-v48-oswal16 %I PMLR %P 1041--1049 %U https://proceedings.mlr.press/v48/oswal16.html %V 48 %X Representational Similarity Learning (RSL) aims to discover features that are important in representing (human-judged) similarities among objects. RSL can be posed as a sparsity-regularized multi-task regression problem. Standard methods, like group lasso, may not select important features if they are strongly correlated with others. To address this shortcoming we present a new regularizer for multitask regression called Group Ordered Weighted \ell_1 (GrOWL). Another key contribution of our paper is a novel application to fMRI brain imaging. Representational Similarity Analysis (RSA) is a tool for testing whether localized brain regions encode perceptual similarities. Using GrOWL, we propose a new approach called Network RSA that can discover arbitrarily structured brain networks (possibly widely distributed and non-local) that encode similarity information. We show, in theory and fMRI experiments, how GrOWL deals with strongly correlated covariates.
RIS
TY - CPAPER TI - Representational Similarity Learning with Application to Brain Networks AU - Urvashi Oswal AU - Christopher Cox AU - Matthew Lambon-Ralph AU - Timothy Rogers AU - Robert Nowak BT - Proceedings of The 33rd International Conference on Machine Learning DA - 2016/06/11 ED - Maria Florina Balcan ED - Kilian Q. Weinberger ID - pmlr-v48-oswal16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 48 SP - 1041 EP - 1049 L1 - http://proceedings.mlr.press/v48/oswal16.pdf UR - https://proceedings.mlr.press/v48/oswal16.html AB - Representational Similarity Learning (RSL) aims to discover features that are important in representing (human-judged) similarities among objects. RSL can be posed as a sparsity-regularized multi-task regression problem. Standard methods, like group lasso, may not select important features if they are strongly correlated with others. To address this shortcoming we present a new regularizer for multitask regression called Group Ordered Weighted \ell_1 (GrOWL). Another key contribution of our paper is a novel application to fMRI brain imaging. Representational Similarity Analysis (RSA) is a tool for testing whether localized brain regions encode perceptual similarities. Using GrOWL, we propose a new approach called Network RSA that can discover arbitrarily structured brain networks (possibly widely distributed and non-local) that encode similarity information. We show, in theory and fMRI experiments, how GrOWL deals with strongly correlated covariates. ER -
APA
Oswal, U., Cox, C., Lambon-Ralph, M., Rogers, T. & Nowak, R.. (2016). Representational Similarity Learning with Application to Brain Networks. Proceedings of The 33rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 48:1041-1049 Available from https://proceedings.mlr.press/v48/oswal16.html.

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