Year: 2012
Type: article
Abstract: We present a method for performing principal component analysis (PCA) on noisy datasets with missing values. Estimates of the measurement error are used to weight the input data such that the resulting eigenvectors, when compared to classic PCA, are more sensitive to the true underlying signal variations rather than being pulled by heteroskedastic measurement noise. Missing data are simply limiting cases of weight = 0. The underlying algorithm is a noise weighted expectation maximization (EM) PCA, which has additional benefits of implementation speed and flexibility for smoothing eigenvectors to reduce the noise contribution. We present applications of this method on simulated data and QSO spectra from the Sloan Digital Sky Survey (SDSS).
DOI: 10.1086/668105
Author: Stephen Bailey
Institution: Lawrence Berkeley National Laboratory
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
Funders: National Science Foundation, U.S. Department of Energy, National Aeronautics and Space Administration, Alfred P. Sloan Foundation, Princeton University, Ohio State University, University of Washington, Johns Hopkins University, University of Pittsburgh, Case Western Reserve University, Drexel University, Universität Basel, University of Portsmouth, New Mexico State University, Chinese Academy of Sciences, Max-Planck-Gesellschaft, Max-Planck-Institut für Astronomie, Office of Science, U.S. Naval Observatory, Fermilab, Los Alamos National Laboratory
OpenAlex ID: W2065072463