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LDA via L1-PCA of Whitened Data

Abstract : Principal component analysis (PCA) and Fisher's linear discriminant analysis (LDA) are widespread techniques in data analysis and pattern recognition. Recently, the L1-norm has been proposed as an alternative criterion to classical L2-norm in PCA, drawing considerable research interest on account of its increased robustness to outliers. The present work proves that, combined with a whitening preprocessing step, L1-PCA can perform LDA in an unsupervised manner, i.e., sparing the need for labelled data. Rigorous proof is given in the case of data drawn from a mixture of Gaussians. A number of numerical experiments on synthetic as well as real data confirm the theoretical findings.
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Contributor : Vicente Zarzoso <>
Submitted on : Friday, February 21, 2020 - 11:42:10 AM
Last modification on : Monday, October 12, 2020 - 10:30:33 AM

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Rubén Martín-Clemente, Vicente Zarzoso. LDA via L1-PCA of Whitened Data. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2020, 68, pp.225-240. ⟨10.1109/TSP.2019.2955860⟩. ⟨hal-02486881⟩



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