Incremental Principal Component Analysis Exact implementation and continuity corrections
Machine Learning
2019-08-14 v2 Machine Learning
Abstract
This paper describes some applications of an incremental implementation of the principal component analysis (PCA). The algorithm updates the transformation coefficients matrix on-line for each new sample, without the need to keep all the samples in memory. The algorithm is formally equivalent to the usual batch version, in the sense that given a sample set the transformation coefficients at the end of the process are the same. The implications of applying the PCA in real time are discussed with the help of data analysis examples. In particular we focus on the problem of the continuity of the PCs during an on-line analysis.
Keywords
Cite
@article{arxiv.1901.07922,
title = {Incremental Principal Component Analysis Exact implementation and continuity corrections},
author = {Vittorio Lippi and Giacomo Ceccarelli},
journal= {arXiv preprint arXiv:1901.07922},
year = {2019}
}
Comments
accepted at http://www.icinco.org/