Semi-sparse PCA

Eldén, Lars and Trendafilov, Nickolay (2019). Semi-sparse PCA. Psychometrika, 84(1) pp. 164–185.

DOI: https://doi.org/10.1007/s11336-018-9650-9

Abstract

It is well-known that the classical exploratory factor analysis (EFA) of data with more observations than variables has several types of indeterminacy. We study the factor indeterminacy and show some new aspects of this problem by considering EFA as a specific data matrix decomposition. We adopt a new approach to the EFA estimation and achieve a new characterization of the factor indeterminacy problem. A new alternative model is proposed, which gives determinate factors and can be seen as a semi-sparse principal component analysis (PCA). An alternating algorithm is developed, where in each step a Procrustes problem is solved. It is demonstrated that the new model/algorithm can act as a specific sparse PCA and as a low-rank-plus-sparse matrix decomposition. Numerical examples with several large data sets illustrate the versatility of the new model, and the performance and behaviour of its algorithmic implementation.

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About

• Item ORO ID
• 57495
• Item Type
• Journal Item
• ISSN
• 1860-0980
• Keywords
• Alternative factor analysis; Matrix decompositions; Least squares; Stiefel manifold; Sparse PCA; Robust PCA
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2018 The Psychometric Society
• Depositing User
• Nickolay Trendafilov