The problem of analysing dynamically evolving textual data has recently arisen. An example of such data is the discussion appearing in Internet chat lines. In this paper a recently introduced source separation method, termed as complexity pursuit, is applied to the problem. The method is a generalisation of projection pursuit to time series and it is able to use both spatial and temporal depend...

In this paper, we review an Artiicial Neural Network we have previously used for extraction of independent signals from a mixture of signals. The network, called the Extended Exploratory Projection Pursuit Network, is shown to be superior to Exploratory Projection Pursuit because of its ability to track time-dependence in its inputs. We give results on artiicial and real data. One of the most i...

Dimension reduction of data, < d ! < p (p << d), to be used for clustering has speciic requirements that are not generally met by generic dimension reduction algorithms such as principal components. Projection pursuit, on the other hand, has a growing variety of criteria that target holes, skewness, etc., using information measures, density functionals, sample moments, etc. With the exception o...

Results are presented for an experiment utilizing land calibration targets imaged by the NRL ultrawideband synthetic aperture radar (NUWSAR). Projection Pursuit statistical analysis tools were applied to a set of simultaneous L and X-band polarimetric images of dihedrals and trihedrals to determine optimal and minimal combinations of polarization and radar bands for identifying different scatte...

Acknowledgements I am most grateful to Professor K.-R.Müller for having provided me the opportunity, not only to gain research experience in his IDA/Machine Learning Laboratory but in addition for employing me to do so. Without his assistance I would not have been able to complete my degree at the BCCN and continue to the PhD level in a dignified manner. I have greatly appreciated having been t...

We present a novel method for finding low dimensional views of high dimensional data: Targeted Projection Pursuit. The method proceeds by finding projections of the data that best approximate a target view. Two versions of the method are introduced; one version based on Procrustes analysis and one based on an artificial neural network. These versions are capable of finding orthogonal or non-ort...

In this paper, we present a projection pursuit (PP) approach to target detection. Unlike most of developed target detection algorithms that require statistical models such as linear mixture, the proposed PP is to project a high dimensional data set into a low dimensional data space while retaining desired information of interest. It utilizes a projection index to explore projections of interest...

The current theory for Independent Component Analysis (ICA) tries to model the observations as unknown linear combination or mixture of N independent components or sources S1(t), . . . , SN(t) whose distribution is also usually unknown. In the ICA problem one tries to recover all the N independent and non-Gaussian components from the only knowledge of the observations. In this paper, we address...

We study the problem of determining the optimal univariate subspace for maximising the separability of a binary partition of unlabeled data, as measured by spectral graph theory. This is achieved by finding projections which minimise the second eigenvalue of the Laplacian matrices of the projected data, which corresponds to a non-convex, non-smooth optimisation problem. We show that the optimal...

A tool is introduced that uses a novel technique to enable users to explore two-dimensional views of high dimensional gene expression data sets. Unlike other such tools, the interface is intuitive and efficient, allowing the user to easily select views that meet their requirements. The tool is tested on publicly available gene expression data sets and demonstrated to find views that show the se...