title: Tensor decompositions for the analysis of atomic resolution electron energy loss spectra.
abstract: A selection of tensor decomposition techniques is presented for the detection of weak signals in electron energy loss spectroscopy (EELS) data. The focus of the analysis lies on the correct representation of the simulated spatial structure. An analysis scheme for EEL spectra combining two-dimensional and n-way decomposition methods is proposed. In particular, the performance of robust principal component analysis (ROBPCA), Tucker Decompositions using orthogonality constraints (Multilinear Singular Value Decomposition (MLSVD)) and Tucker decomposition without imposed constraints, canonical polyadic decomposition (CPD) and block term decompositions (BTD) on synthetic as well as experimental data is examined.
authors.affiliation: Department of Physics and Astronomy, Uppsala University, Box 516, S-751 20 Uppsala, Sweden.
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 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 compression; EELS; Tensor decompositions