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Projection sparse principal component analysis: An efficient least squares method
- Source :
- Journal of Multivariate Analysis. 173:366-382
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- We propose a new sparse principal component analysis (SPCA) method in which the solutions are obtained by projecting the full cardinality principal components onto subsets of variables. The resulting components are guaranteed to explain a given proportion of variance. The computation of these solutions is very efficient. The proposed method compares well with the optimal least squares sparse components. We show that other SPCA methods fail to identify the best sparse approximations of the principal components and explain less variance than our solutions. We illustrate and compare our method with others with extensive simulations and with the analysis of the computational results for nine datasets of increasing dimensions up to 16,000 variables.
- Subjects :
- Statistics and Probability
Numerical Analysis
Dimensionality reduction
020206 networking & telecommunications
Feature selection
02 engineering and technology
Variance (accounting)
01 natural sciences
Least squares
010104 statistics & probability
Cardinality
Power iteration
Principal component analysis
0202 electrical engineering, electronic engineering, information engineering
0101 mathematics
Statistics, Probability and Uncertainty
Projection (set theory)
Algorithm
Mathematics
Subjects
Details
- ISSN :
- 0047259X
- Volume :
- 173
- Database :
- OpenAIRE
- Journal :
- Journal of Multivariate Analysis
- Accession number :
- edsair.doi...........3583d86e9785e28f02f331f04591b544