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tt: Treelet transform with Stata

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  • Anders Gorst-Rasmussen

    (Aalborg University)

Abstract

The treelet transform is a recent data reduction technique from the field of machine learning. Sharing many similarities with principal component analysis, the treelet transform can reduce a multidimensional dataset to the projections on a small number of directions or components that account for much of the variation in the original data. However, in contrast to principal component analysis, the treelet transform produces sparse components. This can greatly simplify interpretation. I describe the tt Stata add-on for performing the treelet transform. The add- on includes a Mata implementation of the treelet transform algorithm alongside other functionality to aid in the practical application of the treelet transform. I demonstrate an example of a basic exploratory data analysis using the tt add-on. Copyright 2012 by StataCorp LP.

Suggested Citation

  • Anders Gorst-Rasmussen, 2012. "tt: Treelet transform with Stata," Stata Journal, StataCorp LP, vol. 12(1), pages 130-146, March.
    • Handle: RePEc:tsj:stataj:v:12:y:2012:i:1:p:130-146
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    References listed on IDEAS

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    1. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    2. Gervini D. & Rousson V., 2004. "Criteria for Evaluating Dimension-Reducing Components for Multivariate Data," The American Statistician, American Statistical Association, vol. 58, pages 72-76, February.
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