In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of th...

The study of natural image statistics considers the statistical properties of large collections of images from natural scenes, and has applications in image processing, computer vision, and visual computational neuroscience. In the past, a major focus in the field of natural image statistics have been the statistics of outputs of linear filters. Recently, attention has been turning to nonlinear...

One way to increase distribution network reliability is to replace traditional overhead lines with underground cables. To fully utilize these investments, network owners will have to adjust their reliability engineering methods to suit the new cable networks. In this paper different condition assessment methods as well as improved failure statistics for cable systems are considered. The paper i...

Ž . Principal component analysis PCA is a well-known data dimensionality technique that has been used to detect faults Ž . during the operation of industrial processes. Dynamic principal component analysis DPCA and canonical variate analysis Ž . CVA are data dimensionality techniques which take into account serial correlations, but their effectiveness in detecting faults in industrial processes...

This paper presents a new application of two dimensional Principal Component Analysis (2DPCA) to the problem of online character recognition in Tamil Script. A novel set of features employing polynomial fits and quartiles in combination with conventional features are derived for each sample point of the Tamil character obtained after smoothing and resampling. These are stacked to form a matrix,...

We develop an image-driven approach to the question of what makes the shape of a woman’s body attractive. We constructed a set of 625 images of female bodies by factorially recombining four independent descriptors of shape derived from a principal components analysis of the variation in natural body shape, and had observers rate these images for attractiveness. We then modelled observers’ attra...

Multivariate statistical analysis is a powerful method of examining complex datasets, such as species assemblages, that does not suffer from the oversimplification prevalent in many univariate analyses. However, identifying whether datapoints on a multivariate plot are clustered is subjective, as there is no determination of significant differences between the points and no indication of the le...