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Projection pursuit (PP) is a type of statistical technique which involves finding the most "interesting" possible projections in multidimensional data. Often, projections which deviate more from a normal distribution are considered to be more interesting. As each projection is found, the data are reduced by removing the component along that projection, and the process is repeated to find new projections; this is the "pursuit" aspect that motivated the technique known as matching pursuit. The idea of projection pursuit is to locate the projection or projections from high-dimensional space to low-dimensional space that reveal the most details about the structure of the data set. Once an interesting set of projections has been found, existing structures (clusters, surfaces, etc.) can be extracted and analyzed separately. Projection pursuit has been widely use for blind source separation, so it is very important in independent component analysis. Projection pursuit seek one projection at a time such that the extracted signal is as non-Gaussian as possible.〔James V. Stone(2004); "Independent Component Analysis: A Tutorial Introduction", The MIT Press Cambridge, Massachusetts, London, England; ISBN 0-262-69315-1〕 == History == Projection pursuit technique were originally proposed and experimented by Kruskal.〔Kruskal, JB. 1969; "Toward a practical method which helps uncover the structure of a set of observations by finding the line transformation which optimizes a new “index of condensation”", Pages 427–440 of: Milton, RC, & Nelder, JA (eds), Statistical computation; New York, Academic Press〕 Related ideas occur in Switzer (1970) and Switzer and Wright (1971). The first successful implementation is due to Jerome H. Friedman and John Tukey (1974), who named projection pursuit. The original purpose of projection pursuit was to machine-pick "interesting" low-dimensional projections of a high-dimensional point cloud by numerically maximizing a certain objective function or projection index. Several years later, Friedman and Stuetzle extended the idea behind projection pursuit and added projection pursuit regression (PPR), projection pursuit classification (PPC), and projection pursuit density estimation (PPDE). 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「projection pursuit」の詳細全文を読む スポンサード リンク
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