2011/12/19 by Mohit Dayal, Dayal, Mohit
Computer Science · Mathematics · #Advanced Statistical Methods and Models #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Methodology (stat.ME) #Target Tracking and Data Fusion in Sensor Networks #stat.ME #stat.ML
paper · pdf · doi:10.48550/arxiv.1112.4321
29 pages, 8 figures
arxiv created 2011/12/19 · openalex publication_date 2011/12/19 · arxiv updated 2011/12/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.