Integrating Data Transformation in Principal Components Analysis

Mehdi Maadooliat, Jianhua Z. Huang, Jianhua Hu

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Principal component analysis (PCA) is a popular dimension reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets. When the data distribution is skewed, data transformation is commonly used prior to applying PCA. Such transformation is usually obtained from previous studies, prior knowledge, or trial-and-error. In this work, we develop a model-based method that integrates data transformation in PCA and finds an appropriate data transformation using the maximum profile likelihood. Extensions of the method to handle functional data and missing values are also developed. Several numerical algorithms are provided for efficient computation. The proposed method is illustrated using simulated and real-world data examples.
Original languageEnglish (US)
Pages (from-to)84-103
Number of pages20
JournalJournal of Computational and Graphical Statistics
Volume24
Issue number1
DOIs
StatePublished - Mar 31 2015
Externally publishedYes

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