The development and use of an approach to three-dimensional projection pursuit are discussed. The well-established Jones and Sibson moments index is chosen as a computationally efficient projection index to extend to three dimensions. The three-dimensional index was initially developed to find interesting linear combinations of spectral bands in a multispectral image. Computer algebraic methods are extensively employed to handle the complex formulae that constitute the index; these methods are explained in detail. A discussion of important practical issues such as interpreting projection solutions, dealing with outliers and optimization techniques completes the description of the index. An artificial tetrahedral data set is used to demonstrate how three-dimensional projection pursuit can produce better clusters than those obtained by principal components analysis. The main example shows how three-demensional projection pursuit can successfully combine bands to discover alternative clusters to those produced by, say, principal components.
Applied Statistics of the Journal of the Royal Statistical Society was founded in 1952. It promotes papers that are driven by real life problems and that make a novel contribution to the subject. JSTOR provides a digital archive of the print version of Applied Statistics. The electronic version of Applied Statistics is available at http://www.interscience.wiley.com. Authorized users may be able to access the full text articles at this site.
Wiley is a global provider of content and content-enabled workflow solutions in areas of scientific, technical, medical, and scholarly research; professional development; and education. Our core businesses produce scientific, technical, medical, and scholarly journals, reference works, books, database services, and advertising; professional books, subscription products, certification and training services and online applications; and education content and services including integrated online teaching and learning resources for undergraduate and graduate students and lifelong learners. Founded in 1807, John Wiley & Sons, Inc. has been a valued source of information and understanding for more than 200 years, helping people around the world meet their needs and fulfill their aspirations. Wiley has published the works of more than 450 Nobel laureates in all categories: Literature, Economics, Physiology or Medicine, Physics, Chemistry, and Peace. Wiley has partnerships with many of the world’s leading societies and publishes over 1,500 peer-reviewed journals and 1,500+ new books annually in print and online, as well as databases, major reference works and laboratory protocols in STMS subjects. With a growing open access offering, Wiley is committed to the widest possible dissemination of and access to the content we publish and supports all sustainable models of access. Our online platform, Wiley Online Library (wileyonlinelibrary.com) is one of the world’s most extensive multidisciplinary collections of online resources, covering life, health, social and physical sciences, and humanities.
This item is part of JSTOR collection
For terms and use, please refer to our Terms and Conditions
Journal of the Royal Statistical Society. Series C (Applied Statistics)
© 1995 Royal Statistical Society
Request Permissions
