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arXiv · 2308.15553

Dimensionality Reduction Using pseudo-Boolean polynomials For Cluster Analysis

Tendai Mapungwana Chikake·Boris Goldengorin
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Abstract

We introduce usage of a reduction property of penalty-based formulation of pseudo-Boolean polynomials as a mechanism for invariant dimensionality reduction in cluster analysis processes. In our experiments, we show that multidimensional data, like 4-dimensional Iris Flower dataset can be reduced to 2-dimensional space while the 30-dimensional Wisconsin Diagnostic Breast Cancer (WDBC) dataset can be reduced to 3-dimensional space, and by searching lines or planes that lie between reduced samples we can extract clusters in a linear and unbiased manner with competitive accuracies, reproducibility and clear interpretation.

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Tendai Mapungwana Chikake, Boris Goldengorin. 2023-08-29. Dimensionality Reduction Using pseudo-Boolean polynomials For Cluster Analysis. https://arxiv.org/abs/2308.15553

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