16,962 research outputs found
Fast Dictionary Learning for Sparse Representations of Speech Signals
© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Published version: IEEE Journal of Selected Topics in Signal Processing 5(5): 1025-1031, Sep 2011. DOI: 10.1109/JSTSP.2011.2157892
A stochastic algorithm for probabilistic independent component analysis
The decomposition of a sample of images on a relevant subspace is a recurrent
problem in many different fields from Computer Vision to medical image
analysis. We propose in this paper a new learning principle and implementation
of the generative decomposition model generally known as noisy ICA (for
independent component analysis) based on the SAEM algorithm, which is a
versatile stochastic approximation of the standard EM algorithm. We demonstrate
the applicability of the method on a large range of decomposition models and
illustrate the developments with experimental results on various data sets.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS499 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Independent components in spectroscopic analysis of complex mixtures
We applied two methods of "blind" spectral decomposition (MILCA and SNICA) to
quantitative and qualitative analysis of UV absorption spectra of several
non-trivial mixture types. Both methods use the concept of statistical
independence and aim at the reconstruction of minimally dependent components
from a linear mixture. We examined mixtures of major ecotoxicants (aromatic and
polyaromatic hydrocarbons), amino acids and complex mixtures of vitamins in a
veterinary drug. Both MICLA and SNICA were able to recover concentrations and
individual spectra with minimal errors comparable with instrumental noise. In
most cases their performance was similar to or better than that of other
chemometric methods such as MCR-ALS, SIMPLISMA, RADICAL, JADE and FastICA.
These results suggest that the ICA methods used in this study are suitable for
real life applications. Data used in this paper along with simple matlab codes
to reproduce paper figures can be found at
http://www.klab.caltech.edu/~kraskov/MILCA/spectraComment: 22 pages, 4 tables, 6 figure
Review of analytical instruments for EEG analysis
Since it was first used in 1926, EEG has been one of the most useful
instruments of neuroscience. In order to start using EEG data we need not only
EEG apparatus, but also some analytical tools and skills to understand what our
data mean. This article describes several classical analytical tools and also
new one which appeared only several years ago. We hope it will be useful for
those researchers who have only started working in the field of cognitive EEG
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