# Dimensionality Reduction Using pseudo-Boolean polynomials For Cluster Analysis

arXiv: https://arxiv.org/abs/2308.15553 · HTML: https://mlsanity.com/paper/2308.15553/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [Toy Models of Superposition](https://mlsanity.com/paper/2209.10652/) (2022)
- [Examining the Use of Neural Networks for Feature Extraction: A Comparative Analysis using Deep Learning, Support Vector Machines, and K-Nearest Neighbor Classifiers](https://mlsanity.com/paper/1805.02294/) (2018)
- [XBNet : An Extremely Boosted Neural Network](https://mlsanity.com/paper/2106.05239/) (2021)

## Abstract (arXiv)

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.