The analysis of heart sound signals is a basic method for heart examination. It may indicate the presence of heart disorders and provide clinical information in the diagnostic process. In this study, a novel feature dimension reduction method based on independent component analysis (ICA) has been proposed for the classification of fourteen different heart sound types; the method was compared wi...

It is common practice to apply linear or nonlinear feature extraction methods before classification. Usually linear methods are faster and simpler than nonlinear ones but an idea successfully employed in the nonlinearization of Support Vector Machines permits a simple and effective extension of several statistical methods to their nonlinear counterparts. In this paper we follow this general non...

The idea of summarizing the information contained in a large number variables by small “factors” or “principal components” has been broadly adopted statistics. This article introduces generalization widely used principal component analysis (PCA) to nonlinear settings, thus providing new tool for dimension reduction and exploratory data representation. distinguishing features method include (i) ...

We introduce a novel fast algorithm for Independent Component Analysis, which can be used for blind source separation and feature extraction. It is shown how a neural network learning rule can be transformed into a xed-point iteration, which provides an algorithm that is very simple, does not depend on any user-deened parameters, and is fast to converge to the most accurate solution allowed by ...

Received July 30, 2014 Revised Sep 14, 2014 Accepted Sep 29, 2014 This paper addresses a new iterative blind carrier frequency offset (CFO) estimator in orthogonal frequency-division multiplexing (OFDM) systems. In our scenario, relying on a hybrid independent component analysis (ICA) scheme, a blind frequency offset estimator is proposed for OFDM systems employing single antenna. The proposed ...

Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon's information-theoretic approach and the projection pursuit approach. Using maximum entropy approximations ...

The performance of standard algorithms for Independent Component Analysis quickly deteriorates under the addition of Gaussian noise. This is partially due to a common first step that typically consists of whitening, i.e., applying Principal Component Analysis (PCA) and rescaling the components to have identity covariance, which is not invariant under Gaussian noise. In our paper we develop the ...