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RESEARCH PRODUCT
Online Principal Component Analysis in High Dimension: Which Algorithm to Choose?
Hervé CardotDavid Degrassubject
Statistics and ProbabilityComputer scienceComputationDimensionality reductionIncremental methods02 engineering and technologyMissing data01 natural sciences010104 statistics & probabilityData explosionStreaming dataPrincipal component analysis0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processing0101 mathematicsStatistics Probability and UncertaintyAlgorithmEigendecomposition of a matrixdescription
Summary Principal component analysis (PCA) is a method of choice for dimension reduction. In the current context of data explosion, online techniques that do not require storing all data in memory are indispensable to perform the PCA of streaming data and/or massive data. Despite the wide availability of recursive algorithms that can efficiently update the PCA when new data are observed, the literature offers little guidance on how to select a suitable algorithm for a given application. This paper reviews the main approaches to online PCA, namely, perturbation techniques, incremental methods and stochastic optimisation, and compares the most widely employed techniques in terms statistical accuracy, computation time and memory requirements using artificial and real data. Extensions of online PCA to missing data and to functional data are detailed. All studied algorithms are available in the package onlinePCA on CRAN.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-07-05 | International Statistical Review |