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Multidimensional projections map data points, defined in a high-dimensional data space, into a 1D, 2D or 3D representation space. Such a mapping may be typically achieved with dimensional reduction, clustering, or force directed point placement. Projections can be displayed and navigated by data analysts by means of visual representations, which may vary from points on a plane to graphs, surfaces or volumes. Typically, projections strive to preserve distance relationships amongst data points, as defined in the original space. Information loss is inevitable and the projection approach defines the extent to which the distance preserving goal is attained. We introduce PEx -the Projection Explorer -a visualization tool for mapping and exploration of high-dimensional data via projections. A set of examples -on both structured (table) and unstructured (text) data -illustrate how projection based visualizations, coupled with appropriate exploration tools, offer a flexible set-up for multidimensional data exploration. The projections in PEx handle relatively large data sets at a computational cost adequate to user interaction.

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Abstract. This paper presents a method for the segmentation and recognition of facial features and face tracking in digital video sequences based on inexact graph matching. It extends a previous approach proposed for static images to video sequences by incorporating the temporal aspect that is inherent to such sequences. Facial features are represented by attributed relational graphs, in which vertices correspond to different feature regions and edges to relations between them. A reference model is used and the search for an optimal homomorphism between its corresponding graph and that of the current frame leads to the recognition.