Complexity pursuit is an extension of projection pursuit to time series data and the method is closely related to blind separation of time-dependent source signals and independent component analysis (ICA). In this paper, we consider the estimation of the data model of ICA when Gaussian noise is present and the independent components are time dependent. We derive a simple algorithm combining Gau...

The role of sparse representations in the context of structured noise filtering is discussed. A strategy, especially conceived so as to address problems of an ill posed nature, is presented. The proposed approach revises and extends the Oblique Matching Pursuit technique. It is shown that, by working with an orthogonal projection of the signal to be filtered, it is possible to apply orthogonal ...

This paper presents two multiresolution segmentation-based algorithms for low bit rate image compression using hierarchical data structures. The segmentation is achieved with quadtree and BSP-tree hierarchical data structures and the encoding is performed by using the projection pursuit (matching pursuit) with a finite dictionary of spline functions with various degrees of smoothness. Compariso...

Particle swarm optimisation (PSO) is introduced to implement a new constructive learning algorithm for training generalised cellular neural networks (GCNNs) for the identification of spatiotemporal evolutionary (STE) systems. The basic idea of the new PSO-based learning algorithm is to successively approximate the desired signal by progressively pursuing relevant orthogonal projections. This ne...

Baseline noise removal from electrocardiogram (ECG) signal is a blind source separation problem. Various noises affect the measured ECG signal. Major ECG noises are baseline noise, electrode contact noise, muscle noise, instrument noise. Baseline noise distorts the low frequency segment of ECG signal. The low frequency segment is s-t segment. This segment is very important and has the informati...

Many data sets are high dimensional. It has been a common practice to use lower dimensional linear projections of the data for visual inspection. The lower dimension is usually 1 or 2 (or maybe 3). More precisely, if X 1 ; : : :; X n 2 IR p are p-dimensional data, then a k (< p)-dimensional linear projection is Z 1 ; : : : ; Z n 2 IR k where Z i = T X i for some p k matrix such that T = I k , t...