In this tutorial, we use NIPALS (Non-linear Iterative Partial Least Squares) algorithm for dimensionality reduction in a proteins discrimination problem. The latent variables produced by nipals become the input variables of a nearest neighbor algorithm. The accuracy of the subsequent classifier is dramatically improved.
NIPALS is a possible implementation of singular value decomposition (SVD); it enables to compute factors (latent variable) of principal component analysis (PCA) without a correlation matrix diagonalization. The computing time is reduced especially when we have dataset with many descriptors.
Keywords: NIPALS, principal component analysis, PCA, K-NN, nearest neighbor, bootstrap
Components: Supervised Learning, NIPALS, K-NN, Bootstrap
Tutorial: en_Tanagra_NIPALS.pdf
Dataset: Tanagra_Nipals.zip
