Projection pursuit is a multivariate statistical technique aimed at detecting interesting low-dimensional data projections. It looks for the data projection which maximizes the projection pursuit index, that is a measure of its interestingness. After an interesting projection is found, it is removed to facilitate the search for other interesting features. Projection pursuit addresses three major challenges of multivariate analysis: the curse of dimensionality, the presence of irrelevant features and the limitations of visual perception. Its applications have been hampered by computational, interpretative and inferential problems. Additional problems arise when data are high-dimensional, that is when there are more variables than units. This session outlines the main features of projection pursuit and its connections with other multivariate techniques. The theory is illustrated with both real and simulated datasets.
Organized by:
Nicola Loperfido (Italy)Invited Speakers:
- Andriette Bekker (South Africa)
Modeling incomplete compositional datasets - Alessandro Berti (Italy)
- Claudio Borroni (Italy)
On the use of some unconventional projection pursuit indexes in cluster identification problems - Manuela Cazzaro (Italy)
Competing projections techniques of multivariate quality data to monitor a production process - Lucio De Capitani (Italy)
On copula-based systems of quantile curves with application in ouliers detection - Bruno Ebner (Germany)
A general maximal projection approach to uniformity testing on the hypersphere - Cinzia Franceschini (Italy)
- Nicola Loperfido (Italy)
- Marco Morosin (The Netherlands)
Modeling high-dimensional data with a multivariate Bernoulli distribution - Boaz Nadler (Israel)
- Perttu Saarela (Finland)
Stationary subspace analysis for spatial data - Tomer Shushi (Israel)