A Principal Components Approach to Combining Regression Estimates

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• 作者：Christopher J. Merz and Michael J. Pazzani
• 关键词：regression ; principal components ; multiple models ; combining estimates
• 刊名：Machine Learning
• 出版年：1999
• 出版时间：1999
• 年：1999
• 卷：36
• 期：1/2
• 页码：9-32
• 全文大小：142 KB

文摘

The goal of combining the predictions of multiple learned models is to form an improved estimator. A combining strategy must be able to robustly handle the inherent correlation, or multicollinearity, of the learned models while identifying the unique contributions of each. A progression of existing approaches and their limitations with respect to these two issues are discussed. A new approach, PCR*, based on principal components regression is proposed to address these limitations. An evaluation of the new approach on a collection of domains reveals that (1) PCR* was the most robust combining method, (2) correlation could be handled without eliminating any of the learned models, and (3) the principal components of the learned models provided a continuum of regularized weights from which PCR* could choose.