Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE)

Feature Extraction Algorithms (FEAs) aim to address the curse of dimensionality that makes machine learning algorithms incompetent. Our study conceptually and empirically explores the most representative FEAs. First, we review the theoretical background of many FEAs from different categories (linear vs. nonlinear, supervised vs. unsupervised, random projection-based vs. manifold-based), present their algorithms, and conduct a conceptual comparison of these methods. Secondly, for three challenging binary and multi-class datasets, we determine the optimal sets of new features and assess the quality of the various transformed feature spaces in terms of statistical significance and power analysis, and the FEA efficacy in terms of classification accuracy and speed.