Structured Sparse Canonical Correlation Analysis

Xi Chen, Liu Han, Jaime Carbonell
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:199-207, 2012.

Abstract

In this paper, we propose to apply sparse canonical correlation analysis (sparse CCA) to an important genome-wide association study problem, eQTL mapping. Existing sparse CCA models do not incorporate structural information among variables such as pathways of genes. This work extends the sparse CCA so that it could exploit either the pre-given or unknown group structure via the structured-sparsity-inducing penalty. Such structured penalty poses new challenge on optimization techniques. To address this challenge, by specializing the excessive gap framework, we develop a scalable primal-dual optimization algorithm with a fast rate of convergence. Empirical results show that the proposed optimization algorithm is more efficient than existing state-of-the-art methods. We also demonstrate the effectiveness of the structured sparse CCA on both simulated and genetic datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v22-chen12a, title = {Structured Sparse Canonical Correlation Analysis}, author = {Chen, Xi and Han, Liu and Carbonell, Jaime}, booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics}, pages = {199--207}, year = {2012}, editor = {Lawrence, Neil D. and Girolami, Mark}, volume = {22}, series = {Proceedings of Machine Learning Research}, address = {La Palma, Canary Islands}, month = {21--23 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v22/chen12a/chen12a.pdf}, url = {https://proceedings.mlr.press/v22/chen12a.html}, abstract = {In this paper, we propose to apply sparse canonical correlation analysis (sparse CCA) to an important genome-wide association study problem, eQTL mapping. Existing sparse CCA models do not incorporate structural information among variables such as pathways of genes. This work extends the sparse CCA so that it could exploit either the pre-given or unknown group structure via the structured-sparsity-inducing penalty. Such structured penalty poses new challenge on optimization techniques. To address this challenge, by specializing the excessive gap framework, we develop a scalable primal-dual optimization algorithm with a fast rate of convergence. Empirical results show that the proposed optimization algorithm is more efficient than existing state-of-the-art methods. We also demonstrate the effectiveness of the structured sparse CCA on both simulated and genetic datasets.} }
Endnote
%0 Conference Paper %T Structured Sparse Canonical Correlation Analysis %A Xi Chen %A Liu Han %A Jaime Carbonell %B Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2012 %E Neil D. Lawrence %E Mark Girolami %F pmlr-v22-chen12a %I PMLR %P 199--207 %U https://proceedings.mlr.press/v22/chen12a.html %V 22 %X In this paper, we propose to apply sparse canonical correlation analysis (sparse CCA) to an important genome-wide association study problem, eQTL mapping. Existing sparse CCA models do not incorporate structural information among variables such as pathways of genes. This work extends the sparse CCA so that it could exploit either the pre-given or unknown group structure via the structured-sparsity-inducing penalty. Such structured penalty poses new challenge on optimization techniques. To address this challenge, by specializing the excessive gap framework, we develop a scalable primal-dual optimization algorithm with a fast rate of convergence. Empirical results show that the proposed optimization algorithm is more efficient than existing state-of-the-art methods. We also demonstrate the effectiveness of the structured sparse CCA on both simulated and genetic datasets.
RIS
TY - CPAPER TI - Structured Sparse Canonical Correlation Analysis AU - Xi Chen AU - Liu Han AU - Jaime Carbonell BT - Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics DA - 2012/03/21 ED - Neil D. Lawrence ED - Mark Girolami ID - pmlr-v22-chen12a PB - PMLR DP - Proceedings of Machine Learning Research VL - 22 SP - 199 EP - 207 L1 - http://proceedings.mlr.press/v22/chen12a/chen12a.pdf UR - https://proceedings.mlr.press/v22/chen12a.html AB - In this paper, we propose to apply sparse canonical correlation analysis (sparse CCA) to an important genome-wide association study problem, eQTL mapping. Existing sparse CCA models do not incorporate structural information among variables such as pathways of genes. This work extends the sparse CCA so that it could exploit either the pre-given or unknown group structure via the structured-sparsity-inducing penalty. Such structured penalty poses new challenge on optimization techniques. To address this challenge, by specializing the excessive gap framework, we develop a scalable primal-dual optimization algorithm with a fast rate of convergence. Empirical results show that the proposed optimization algorithm is more efficient than existing state-of-the-art methods. We also demonstrate the effectiveness of the structured sparse CCA on both simulated and genetic datasets. ER -
APA
Chen, X., Han, L. & Carbonell, J.. (2012). Structured Sparse Canonical Correlation Analysis. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 22:199-207 Available from https://proceedings.mlr.press/v22/chen12a.html.

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