Dataset Information

Integrating Data Transformation in Principal Components Analysis.

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.

SUBMITTER: Maadooliat M

PROVIDER: S-EPMC4408558 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Publications

Integrating Data Transformation in Principal Components Analysis.

Maadooliat Mehdi M Huang Jianhua Z JZ Hu Jianhua J

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 20150101 1

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 usi ...[more]

PMID: 25914514

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Dataset Information

Integrating Data Transformation in Principal Components Analysis.

Publications

Integrating Data Transformation in Principal Components Analysis.

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