The R package onlinePCA implements algorithms for online Principal Components Analysis and convenience functions for updating sample means and covariances. The provided algorithms are:
- Recursive PCA based on a perturbation approach
- Recursive PCA based on secular equations
- Stochastic gradient algorithms (Stochastic Gradient Ascent and Subspace Networl Learning) with exact or neural networl implementation
References:
- Gu, M. and Eisenstat, S. C. (1994). A stable and efficient algorithm for the rank-one modification of the symmetric eigenproblem. SIAM Journal of Matrix Analysis and Applications.
- Hegde et al. (2006) Perturbation-Based Eigenvector Updates for On-Line Principal Components Analysis and Canonical Correlation Analysis. Journal of VLSI Signal Processing
- Li, W., Yue, H. H., Valles-Cervantes, S. and Qin, S. J. (2000). Recursive PCA for adaptive process monitoring. Journal of Process Control.
- Oja, E. (1992). Principal components, Minor components, and linear neural networks. Neural Networks.