36,015 research outputs found
Efficient independent component analysis
Independent component analysis (ICA) has been widely used for blind source
separation in many fields such as brain imaging analysis, signal processing and
telecommunication. Many statistical techniques based on M-estimates have been
proposed for estimating the mixing matrix. Recently, several nonparametric
methods have been developed, but in-depth analysis of asymptotic efficiency has
not been available. We analyze ICA using semiparametric theories and propose a
straightforward estimate based on the efficient score function by using
B-spline approximations. The estimate is asymptotically efficient under
moderate conditions and exhibits better performance than standard ICA methods
in a variety of simulations.Comment: Published at http://dx.doi.org/10.1214/009053606000000939 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Heavy-tailed Independent Component Analysis
Independent component analysis (ICA) is the problem of efficiently recovering
a matrix from i.i.d. observations of
where is a random vector with mutually independent
coordinates. This problem has been intensively studied, but all existing
efficient algorithms with provable guarantees require that the coordinates
have finite fourth moments. We consider the heavy-tailed ICA problem
where we do not make this assumption, about the second moment. This problem
also has received considerable attention in the applied literature. In the
present work, we first give a provably efficient algorithm that works under the
assumption that for constant , each has finite
-moment, thus substantially weakening the moment requirement
condition for the ICA problem to be solvable. We then give an algorithm that
works under the assumption that matrix has orthogonal columns but requires
no moment assumptions. Our techniques draw ideas from convex geometry and
exploit standard properties of the multivariate spherical Gaussian distribution
in a novel way.Comment: 30 page
Fourth Moments and Independent Component Analysis
In independent component analysis it is assumed that the components of the
observed random vector are linear combinations of latent independent random
variables, and the aim is then to find an estimate for a transformation matrix
back to these independent components. In the engineering literature, there are
several traditional estimation procedures based on the use of fourth moments,
such as FOBI (fourth order blind identification), JADE (joint approximate
diagonalization of eigenmatrices), and FastICA, but the statistical properties
of these estimates are not well known. In this paper various independent
component functionals based on the fourth moments are discussed in detail,
starting with the corresponding optimization problems, deriving the estimating
equations and estimation algorithms, and finding asymptotic statistical
properties of the estimates. Comparisons of the asymptotic variances of the
estimates in wide independent component models show that in most cases JADE and
the symmetric version of FastICA perform better than their competitors.Comment: Published at http://dx.doi.org/10.1214/15-STS520 in the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Independent Component Analysis of Spatiotemporal Chaos
Two types of spatiotemporal chaos exhibited by ensembles of coupled nonlinear
oscillators are analyzed using independent component analysis (ICA). For
diffusively coupled complex Ginzburg-Landau oscillators that exhibit smooth
amplitude patterns, ICA extracts localized one-humped basis vectors that
reflect the characteristic hole structures of the system, and for nonlocally
coupled complex Ginzburg-Landau oscillators with fractal amplitude patterns,
ICA extracts localized basis vectors with characteristic gap structures.
Statistics of the decomposed signals also provide insight into the complex
dynamics of the spatiotemporal chaos.Comment: 5 pages, 6 figures, JPSJ Vol 74, No.
Quantifying identifiability in independent component analysis
We are interested in consistent estimation of the mixing matrix in the ICA
model, when the error distribution is close to (but different from) Gaussian.
In particular, we consider independent samples from the ICA model , where we assume that the coordinates of are independent
and identically distributed according to a contaminated Gaussian distribution,
and the amount of contamination is allowed to depend on . We then
investigate how the ability to consistently estimate the mixing matrix depends
on the amount of contamination. Our results suggest that in an asymptotic
sense, if the amount of contamination decreases at rate or faster,
then the mixing matrix is only identifiable up to transpose products. These
results also have implications for causal inference from linear structural
equation models with near-Gaussian additive noise.Comment: 22 pages, 2 figure
Statistical physics of independent component analysis
Statistical physics is used to investigate independent component analysis
with polynomial contrast functions. While the replica method fails, an adapted
cavity approach yields valid results. The learning curves, obtained in a
suitable thermodynamic limit, display a first order phase transition from poor
to perfect generalization.Comment: 7 pages, 1 figure, to appear in Europhys. Lett
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