Supervised projection pursuit - A dimensionality reduction technique optimized for probabilistic classification

Barcaru, A., 15-Nov-2019, In : Chemometrics and Intelligent Laboratory Systems. 194, 11 p., 103867.

Research output: Contribution to journalArticleAcademicpeer-review

An important step in multivariate analysis is the dimensionality reduction, which allows for a better classification and easier visualization of the class structures in the data. Techniques like PCA, PLS-DA and LDA are most often used to explore the patterns in the data and to reduce the dimensions. Yet the data does not always reveal properly the structures when these techniques are applied. To this end, a supervised projection pursuit (SuPP) is proposed in this article, based on Jensen-Shannon divergence. The combination of this metric with powerful Monte Carlo based optimization algorithm, yielded a versatile dimensionality reduction technique capable of working with highly dimensional data and missing observations. Combined with Naïve Bayes (NB) classifier, SuPP proved to be a powerful preprocessing tool for classification. Namely, on the Iris data set, the prediction accuracy of SuPP-NB is significantly higher than the prediction accuracy of PCA-NB, (p-value ≤ 4.02E-05 in a 2D latent space, p-value ≤ 3.00E-03 in a 3D latent space) and significantly higher than the prediction accuracy of PLS-DA (p-value ≤ 1.17E-05 in a 2D latent space and p-value ≤ 3.08E-03 in a 3D latent space). The significantly higher accuracy for this particular data set is a strong evidence of a better class separation in the latent spaces obtained with SuPP.
Original languageEnglish
Article number103867
Number of pages11
JournalChemometrics and Intelligent Laboratory Systems
Publication statusPublished - 15-Nov-2019


  • Projection methods, Dimension reduction, probabilistic classification, Bayes classification, projection pursuit, Discriminant Analysis

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