81,275 research outputs found
New Negentropy Optimization Schemes for Blind Signal Extraction of Complex Valued Sources
Blind signal extraction, a hot issue in the field of communication signal processing, aims to retrieve the sources through the optimization of contrast functions. Many contrasts based on higher-order statistics such as kurtosis, usually behave sensitive to outliers. Thus, to achieve robust results, nonlinear functions are utilized as contrasts to approximate the negentropy criterion, which is also a classical metric for non-Gaussianity. However, existing methods generally have a high computational cost, hence leading us to address the problem of efficient optimization of contrast function. More precisely, we design a novel “reference-based” contrast function based on negentropy approximations, and then propose a new family of algorithms (Alg.1 and Alg.2) to maximize it. Simulations confirm the convergence of our method to a separating solution, which is also analyzed in theory. We also validate the theoretic complexity analysis that Alg.2 has a much lower computational cost than Alg.1 and existing optimization methods based on negentropy criterion. Finally, experiments for the separation of single sideband signals illustrate that our method has good prospects in real-world applications
Of `Cocktail Parties' and Exoplanets
The characterisation of ever smaller and fainter extrasolar planets requires
an intricate understanding of one's data and the analysis techniques used.
Correcting the raw data at the 10^-4 level of accuracy in flux is one of the
central challenges. This can be difficult for instruments that do not feature a
calibration plan for such high precision measurements. Here, it is not always
obvious how to de-correlate the data using auxiliary information of the
instrument and it becomes paramount to know how well one can disentangle
instrument systematics from one's data, given nothing but the data itself. We
propose a non-parametric machine learning algorithm, based on the concept of
independent component analysis, to de-convolve the systematic noise and all
non-Gaussian signals from the desired astrophysical signal. Such a `blind'
signal de-mixing is commonly known as the `Cocktail Party problem' in
signal-processing. Given multiple simultaneous observations of the same
exoplanetary eclipse, as in the case of spectrophotometry, we show that we can
often disentangle systematic noise from the original light curve signal without
the use of any complementary information of the instrument. In this paper, we
explore these signal extraction techniques using simulated data and two data
sets observed with the Hubble-NICMOS instrument. Another important application
is the de-correlation of the exoplanetary signal from time-correlated stellar
variability. Using data obtained by the Kepler mission we show that the desired
signal can be de-convolved from the stellar noise using a single time series
spanning several eclipse events. Such non-parametric techniques can provide
important confirmations of the existent parametric corrections reported in the
literature, and their associated results. Additionally they can substantially
improve the precision exoplanetary light curve analysis in the future.Comment: ApJ accepte
Overlearning in marginal distribution-based ICA: analysis and solutions
The present paper is written as a word of caution, with users of
independent component analysis (ICA) in mind, to overlearning
phenomena that are often observed.\\
We consider two types of overlearning, typical to high-order
statistics based ICA. These algorithms can be seen to maximise the
negentropy of the source estimates. The first kind of overlearning
results in the generation of spike-like signals, if there are not
enough samples in the data or there is a considerable amount of
noise present. It is argued that, if the data has power spectrum
characterised by curve, we face a more severe problem, which
cannot be solved inside the strict ICA model. This overlearning is
better characterised by bumps instead of spikes. Both overlearning
types are demonstrated in the case of artificial signals as well as
magnetoencephalograms (MEG). Several methods are suggested to
circumvent both types, either by making the estimation of the ICA
model more robust or by including further modelling of the data
From neural PCA to deep unsupervised learning
A network supporting deep unsupervised learning is presented. The network is
an autoencoder with lateral shortcut connections from the encoder to decoder at
each level of the hierarchy. The lateral shortcut connections allow the higher
levels of the hierarchy to focus on abstract invariant features. While standard
autoencoders are analogous to latent variable models with a single layer of
stochastic variables, the proposed network is analogous to hierarchical latent
variables models. Learning combines denoising autoencoder and denoising sources
separation frameworks. Each layer of the network contributes to the cost
function a term which measures the distance of the representations produced by
the encoder and the decoder. Since training signals originate from all levels
of the network, all layers can learn efficiently even in deep networks. The
speedup offered by cost terms from higher levels of the hierarchy and the
ability to learn invariant features are demonstrated in experiments.Comment: A revised version of an article that has been accepted for
publication in Advances in Independent Component Analysis and Learning
Machines (2015), edited by Ella Bingham, Samuel Kaski, Jorma Laaksonen and
Jouko Lampine
Blind image separation based on exponentiated transmuted Weibull distribution
In recent years the processing of blind image separation has been
investigated. As a result, a number of feature extraction algorithms for direct
application of such image structures have been developed. For example,
separation of mixed fingerprints found in any crime scene, in which a mixture
of two or more fingerprints may be obtained, for identification, we have to
separate them. In this paper, we have proposed a new technique for separating a
multiple mixed images based on exponentiated transmuted Weibull distribution.
To adaptively estimate the parameters of such score functions, an efficient
method based on maximum likelihood and genetic algorithm will be used. We also
calculate the accuracy of this proposed distribution and compare the
algorithmic performance using the efficient approach with other previous
generalized distributions. We find from the numerical results that the proposed
distribution has flexibility and an efficient resultComment: 14 pages, 12 figures, 4 tables. International Journal of Computer
Science and Information Security (IJCSIS),Vol. 14, No. 3, March 2016 (pp.
423-433
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