Probabilistic Auto-Associative Models and Semi-Linear PCA

Auto-Associative models cover a large class of methods used in data analysis. In this paper, we describe the generals properties of these models when the projection component is linear and we propose and test an easy to implement Probabilistic Semi-Linear Auto- Associative model in a Gaussian setting. We show it is a generalization of the PCA model to the semi-linear case. Numerical experiments on simulated datasets and a real astronomical application highlight the interest of this approac

A New Algorithm for Exploratory Projection Pursuit

In this paper, we propose a new algorithm for exploratory projection pursuit. The basis of the algorithm is the insight that previous approaches used fairly narrow definitions of interestingness / non interestingness. We argue that allowing these definitions to depend on the problem / data at hand is a more natural approach in an exploratory technique. This also allows our technique much greater applicability than the approaches extant in the literature. Complementing this insight, we propose a class of projection indices based on the spatial distribution function that can make use of such information. Finally, with the help of real datasets, we demonstrate how a range of multivariate exploratory tasks can be addressed with our algorithm. The examples further demonstrate that the proposed indices are quite capable of focussing on the interesting structure in the data, even when this structure is otherwise hard to detect or arises from very subtle patterns.Comment: 29 pages, 8 figure

Interactive data exploration with targeted projection pursuit

Data exploration is a vital, but little considered, part of the scientific process; but few visualisation tools can cope with truly complex data. Targeted Projection Pursuit (TPP) is an interactive data exploration technique that provides an intuitive and transparent interface for data exploration. A prototype has been evaluated quantitatively and found to outperform algorithmic techniques on standard visual analysis tasks

Projection Pursuit for Exploratory Supervised Classification

In high-dimensional data, one often seeks a few interesting low-dimensional projections that reveal important features of the data. Projection pursuit is a procedure for searching high-dimensional data for interesting low-dimensional projections via the optimization of a criterion function called the projection pursuit index. Very few projection pursuit indices incorporate class or group information in the calculation. Hence, they cannot be adequately applied in supervised classification problems to provide low-dimensional projections revealing class differences in the data . We introduce new indices derived from linear discriminant analysis that can be used for exploratory supervised classification.Data mining, Exploratory multivariate data analysis, Gene expression data, Discriminant analysis

Relations between Tourism and Sport in the Context of Tourism as an Academic Discipline

The specific objective of the paper is to discuss the mutual relations between tourism and sport and, in a wider context, to draw the reader’s attention to the potentially excessive range of research goals in tourism as a discipline. Within the scope of discussion, the author looks at tourism as a social activity and a conceptual and research subject. Research questions, the signposts of intellectual debate, come down to whether tourism shares any common areas with sport (in its widest sense). If so, is such activity still tourism activity? Or perhaps these types of ‘sport-tourist’ activities should be excluded from discussion on tourism as an academic discipline because of their non-tourist character? The author assumes that there is an exploratory and cognitive zone between these two areas of social activity, going beyond both tourism and sport. Tourist activity and sport activity in fact differ from each other