Comparison of Dimension Reduction Algorithms on the Example of Hard Disk State Classifiers

This article discusses data dimension reduction algorithms in the framework of machine learning problems. Dimension reduction algorithms such as PCA, t-sne, UMAP are considered. The efficiency of these algorithms has been tested within the framework of the problem of classifying the states of hard disks. The classifiers are built on the basis of such machine learning tools as: SVM, RF, LR, KNN, LSTM. Dimension reduction algorithms are compared based on the results of the models trained on the data transformed by the selected algorithms.