2006/02/15 by Eric Bair, Trevor Hastie, Debashis Paul +1 · 833 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · #Artificial intelligence #Computer science #Consistency (knowledge bases) #Covariate #Data mining #Gene expression and cancer classification #Machine learning #Mathematics #Principal (computer security) #Principal component analysis #Principal component regression #Regression #Regression analysis #Statistics
paper · doi:10.1198/016214505000000628
published in Journal of the American Statistical Association 101(473), 119-137
openalex publication_date 2006/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called supervised principal components that can be applied to this type of problem. Supervised principal components is similar to conventional principal components analysis except that it uses a subset of the predictors selected based on their association with the outcome. Supervised principal components can be applied to regression and generalized regression problems, such as survival analysis. It compares favorably to other techniques for this type of problem, and can also account for the effects of other covariates and help identify which predictor variables are most important. We also provide asymptotic consistency results to help support our empirical findings. These methods could become important tools for DNA microarray data, where they may be used to more accurately diagnose and treat cancer.