arXiv Machine Learning September 24, 2026 By Ousmane Assani Amate, Elyes Lounissi, Mohammadreza Bakhtyari, \'Emilie Roy, Roman Sarrazin-Gendron, Vladimir Makarenkov

Assessing the impact of dimensionality reduction on clustering performance - a systematic study

Read the original on arXiv Machine Learning →

The paper systematically evaluates how five dimensionality reduction methods—PCA, Kernel PCA, VAE, Isomap, and MDS—affect the performance of four clustering algorithms (k‑means, AHC, GMM, and OPTICS). Using the Adjusted Rand Index, the study compares clustering quality with and without dimensionality reduction at levels of k‑1, 25%, and 50% of the original dimensions. Results highlight that the choice of reduction technique and its level must be carefully matched to the data’s geometry and the clustering algorithm used.

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