Simple Poisson PCA: an algorithm for (sparse) feature extraction with simultaneous dimension determination

Luke Smallman (), William Underwood and Andreas Artemiou
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Luke Smallman: Cardiff University
William Underwood: University of Oxford
Andreas Artemiou: Cardiff University

Computational Statistics, 2020, vol. 35, issue 2, No 7, 559-577

Abstract: Abstract Dimension reduction tools offer a popular approach to analysis of high-dimensional big data. In this paper, we propose an algorithm for sparse Principal Component Analysis for non-Gaussian data. Since our interest for the algorithm stems from applications in text data analysis we focus on the Poisson distribution which has been used extensively in analysing text data. In addition to sparsity our algorithm is able to effectively determine the desired number of principal components in the model (order determination). The good performance of our proposal is demonstrated with both synthetic and real data examples.

Keywords: L0 penalty; Exponential family; Text data analysis; Dimension reduction (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s00180-019-00903-0

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