Integrative linear discriminant analysis with guaranteed error rate improvement

• 成果类型: Article
• 署名作者: Li, Quefeng; Li, Lexin
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of California System; University of California Berkeley
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asy047
• 发表日期: 2018
• 页码: 917930
• 关键词: early alzheimers-disease; selection; atrophy

摘要

Multiple types of data measured on a common set of subjects arise in many areas. Numerous empirical studies have found that integrative analysis of such data can result in better statistical performance in terms of prediction and feature selection. However, the advantages of integrative analysis have mostly been demonstrated empirically. In the context of two-class classification, we propose an integrative linear discriminant analysis method and establish a theoretical guarantee that it achieves a smaller classification error than running linear discriminant analysis on each data type individually. We address the issues of outliers and missing values, frequently encountered in integrative analysis, and illustrate our method through simulations and a neuroimaging study of Alzheimer's disease.