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Activity Number: 623
Type: Invited
Date/Time: Thursday, August 13, 2015 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Graphics
Abstract #314530
Title: Independent Component Analysis for Spatial Stochastic Processes on a Lattice
Author(s): Haipeng Shen*
Companies: The University of North Carolina at Chapel Hill
Keywords: Spatial dependence ; spectral analysis ; Whittle likelihood
Abstract:

Independent Component Analysis (ICA) is a widespread data-driven methodology used to solve Blind Source Separation problems. A lot of algorithms have been proposed to perform ICA, but few of them take into account the dependence within the mixtures. Some algorithms deal with the temporal ICA (tICA) approach exploiting the temporal autocorrelation of the mixtures (and the sources). In particular, colored ICA (cICA), that works in the spectral domain, is an effective method to perform ICA through a Whittle likelihood procedure assuming the sources to be temporal stochastic process. However spatial ICA (sICA) approach is becoming dominant in several fields, like fMRI analysis or geo-referred imaging. We present an extension of cICA, called spatial colored ICA (scICA), where sources are assumed to be spatial stochastic processes on a lattice. We exploit the Whittle likelihood and a kernel based nonparametric algorithm to estimate the spectral density of a spatial process on a lattice. We illustrate the performance of the proposed method through different simulation studies and real applications.


Authors who are presenting talks have a * after their name.

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