title: Analysis of electron energy loss spectroscopy data using geometric extraction methods.
abstract: A set of geometric data decomposition methods is discussed. In particular, randomized vertex component analysis (RVCA), an extension of vertex component analysis (VCA) for the application to noisy data, is established. Minimum volume simplex analysis (MVSA), a recent technique for the extraction of endmembers in the absence of pure pixels, is presented. A comparison between MVSA and the previously presented technique of Bayesian Linear Unmixing (BLU) is drawn. Lastly, the efficiency of these methods for high-dimensional data is examined. Improvement on the extracted source components spectral signatures are achieved by establishing Gaussian mixture modeling as extraction technique.
authors.affiliation: Department of Physics and Astronomy, Uppsala University, Box 516, S-751 20 Uppsala, Sweden. Electronic address: jakob.spiegelberg@physics.uu.se.
authors.forename: Jakob
authors.initials: J
authors.lastname: Spiegelberg
authors.affiliation: Department of Physics and Astronomy, Uppsala University, Box 516, S-751 20 Uppsala, Sweden.
authors.forename: Ján
authors.initials: J
authors.lastname: Rusz
authors.affiliation: Department of Engineering Sciences, Uppsala University, Box 534, S-751 21 Uppsala, Sweden.
authors.forename: Thomas
authors.initials: T
authors.lastname: Thersleff
authors.affiliation: Department of Information Technology, Uppsala University, Box 337, S-751 05 Uppsala, Sweden.
authors.forename: Kristiaan
authors.initials: K
authors.lastname: Pelckmans
keywords: Blind source separation; Data clustering; EELS; Geometric extraction methods; RVCA