Vines, S. K.
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We introduce an algorithm for producing simple approximate principal components directly from a variance-covariance matrix. At the heart of the algorithm is a series of `simplicity preserving' linear transformations. Each transformation seeks a direction within a two-dimensional subspace that has maximum variance. However, the choice of directions is limited so that the direction can be represented by a vector of integers whenever the subspace can also be represented by vectors of integers. The resulting approximate components can therefore always be represented by integers. Furthermore the elements of these integer vectors are often small, particularly for the first few components. We demonstrate the performance of this algorithm on two data sets and show that good approximations to the principal components that are also clearly simple and interpretable can result.
|Item Type:||Journal Article|
|Copyright Holders:||2000 Royal Statistical Society|
|Keywords:||interpretation; pairwise linear transformation; principal components analysis; simplification|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Sarah Frain|
|Date Deposited:||05 Apr 2011 15:08|
|Last Modified:||04 Oct 2016 10:46|
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