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A note on the structure of the quadratic subspace in discriminant analysis

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  • Velilla, Santiago

Abstract

This paper explores some properties of the quadratic subspace, a tool for dimension reduction in discriminant analysis (Velilla, 2008, 2010). This linear manifold has a fairly complex structure, and it may sometimes include components with both mean and covariance separation properties. In this case, an assumption of orthogonality between the leading location directions and the bulk of the dispersion subspaces can help to find an adequate directional representation of it in practice. Two real data sets are analyzed.

Suggested Citation

  • Velilla, Santiago, 2012. "A note on the structure of the quadratic subspace in discriminant analysis," Statistics & Probability Letters, Elsevier, vol. 82(4), pages 739-747.
    • Handle: RePEc:eee:stapro:v:82:y:2012:i:4:p:739-747
    DOI: 10.1016/j.spl.2011.12.020
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    References listed on IDEAS

    as
    1. Velilla, Santiago, 2010. "On the structure of the quadratic subspace in discriminant analysis," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1239-1251, May.
    2. Li, Bing & Wang, Shaoli, 2007. "On Directional Regression for Dimension Reduction," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 997-1008, September.
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