Orthogonal Subspace Clustering: Enhancing High-Dimensional Data Analysis through Adaptive Dimensionality Reduction and Efficient Clustering

TL;DR

This paper introduces Orthogonal Subspace Clustering (OSC), a novel high-dimensional data clustering method that leverages a new theoretical foundation to improve clustering accuracy and efficiency through adaptive dimensionality reduction.

Contribution

The paper provides a theoretical theorem linking high-dimensional data to orthogonal subspaces and develops the OSC framework that adaptively reduces dimensions for better clustering performance.

Findings

OSC significantly improves clustering accuracy on benchmark datasets.

The method enhances robustness and efficiency in high-dimensional data analysis.

Experimental results outperform existing clustering techniques.

Abstract

This paper presents Orthogonal Subspace Clustering (OSC), an innovative method for high-dimensional data clustering. We first establish a theoretical theorem proving that high-dimensional data can be decomposed into orthogonal subspaces in a statistical sense, whose form exactly matches the paradigm of Q-type factor analysis. This theorem lays a solid mathematical foundation for dimensionality reduction via matrix decomposition and factor analysis. Based on this theorem, we propose the OSC framework to address the "curse of dimensionality" -- a critical challenge that degrades clustering effectiveness due to sample sparsity and ineffective distance metrics. OSC integrates orthogonal subspace construction with classical clustering techniques, introducing a data-driven mechanism to select the subspace dimension based on cumulative variance contribution. This avoids manual selection biases while maximizing the retention of discriminative information. By projecting high-dimensional data into an uncorrelated, low-dimensional orthogonal subspace, OSC significantly improves clustering efficiency, robustness, and accuracy. Extensive experiments on various benchmark datasets demonstrate the effectiveness of OSC, with thorough analysis of evaluation metrics including Cluster Accuracy (ACC), Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI) highlighting its advantages over existing methods.