【摘 要】
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The importance of dimension reduction for predictive modeling and visualization plays a central role in statistical graphics and computation.This interest h
【机 构】
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UniversityofIllinoisatUrbana-Champaign
【出 处】
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IMS-China International Conference on Statistics and Probabi
论文部分内容阅读
The importance of dimension reduction for predictive modeling and visualization plays a central role in statistical graphics and computation.This interest has been revived recently in the machine learning literature in the context of manifold learn ing algorithms.In this talk, Ill present a new dimension reduction algorithm for regression/classification models.Our algorithm is developed upon the sliced inverse regression model with a utilization of the local manifold structure of the data.The utility of our algorithm with respect to predictive accuracy as well as exploratory data analysis via visualization will be demonstrated on a variety of simulated and real data.
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