2015 OptimalKernelGroupTransformatio

• (Chao et al., 2015) ⇒ Pan Chao, Qiming Huang, and Michael Zhu. (2015). “Optimal Kernel Group Transformation for Exploratory Regression Analysis and Graphics.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783327

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Cited By

• http://scholar.google.com/scholar?q=%222015%22+Optimal+Kernel+Group+Transformation+for+Exploratory+Regression+Analysis+and+Graphics
• http://dl.acm.org/citation.cfm?id=2783258.2783327&preflayout=flat#citedby

Quotes

Author Keywords

• Conditional covariance operator; eigen decomposition; kernel method; knowledge acquisition; methodology and techniques; optimal transformation; regression analysis

Abstract

The general goal of multivariate regression analysis is to infer about the relationship between a response variable Y and a predictor vector X. Many popularly used regression methods only focus on specific aspects of this relationship. Even though the conditional distribution X) of Y given X fully characterizes the relationship between Y and X, estimation of the conditional density is challenging and becomes quickly infeasible when the dimension of X increases. In this article, we propose to use optimal group transformations as a general approach for exploring the relationship between Y and X. This approach can be considered an automatic procedure to identify the best characteristic of P (Y|X) under which the relationship between Y and X can be fully explored. The emphasis on using group transformations allows the approach to recover intrinsic group structures among the predictors. Furthermore, we develop kernel methods for estimating the optimal group transformations based on cross-covariance and conditional covariance operators. The statistical consistency of the estimates has been established. We refer to the proposed framework and approach as the Optimal Kernel Group Transformation (OKGT) method. Simulation study and real data applications show that the OKGT method is flexible and powerful for the general purpose of high dimensional regression analysis.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 OptimalKernelGroupTransformatio	Pan Chao Qiming Huang Michael Zhu			Optimal Kernel Group Transformation for Exploratory Regression Analysis and Graphics				10.1145/2783258.2783327		2015