6,357 research outputs found
Multi-Detector Multi-Component spectral matching and applications for CMB data analysis
We present a new method for analyzing multi--detector maps containing
contributions from several components. Our method, based on matching the data
to a model in the spectral domain, permits to estimate jointly the spatial
power spectra of the components and of the noise, as well as the mixing
coefficients. It is of particular relevance for the analysis of
millimeter--wave maps containing a contribution from CMB anisotropies.Comment: 15 pages, 7 Postscript figures, submitted to MNRA
Modeling sparse connectivity between underlying brain sources for EEG/MEG
We propose a novel technique to assess functional brain connectivity in
EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA),
can overcome the problem of volume conduction by modeling neural data
innovatively with the following ingredients: (a) the EEG is assumed to be a
linear mixture of correlated sources following a multivariate autoregressive
(MVAR) model, (b) the demixing is estimated jointly with the source MVAR
parameters, (c) overfitting is avoided by using the Group Lasso penalty. This
approach allows to extract the appropriate level cross-talk between the
extracted sources and in this manner we obtain a sparse data-driven model of
functional connectivity. We demonstrate the usefulness of SCSA with simulated
data, and compare to a number of existing algorithms with excellent results.Comment: 9 pages, 6 figure
Independent Process Analysis without A Priori Dimensional Information
Recently, several algorithms have been proposed for independent subspace
analysis where hidden variables are i.i.d. processes. We show that these
methods can be extended to certain AR, MA, ARMA and ARIMA tasks. Central to our
paper is that we introduce a cascade of algorithms, which aims to solve these
tasks without previous knowledge about the number and the dimensions of the
hidden processes. Our claim is supported by numerical simulations. As a
particular application, we search for subspaces of facial components.Comment: 9 pages, 2 figure
Semi-blind Bayesian inference of CMB map and power spectrum
We present a new blind formulation of the Cosmic Microwave Background (CMB)
inference problem. The approach relies on a phenomenological model of the
multi-frequency microwave sky without the need for physical models of the
individual components. For all-sky and high resolution data, it unifies parts
of the analysis that have previously been treated separately, such as component
separation and power spectrum inference. We describe an efficient sampling
scheme that fully explores the component separation uncertainties on the
inferred CMB products such as maps and/or power spectra. External information
about individual components can be incorporated as a prior giving a flexible
way to progressively and continuously introduce physical component separation
from a maximally blind approach. We connect our Bayesian formalism to existing
approaches such as Commander, SMICA and ILC, and discuss possible future
extensions.Comment: 11 pages, 9 figure
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