CCP: Correlated Clustering and Projection for Dimensionality Reduction

2022/06/08 by Yuta Hozumi, Rui Wang, Hozumi, Yuta +3
Computer Science · Mathematics · Medicine · #Advanced Neuroimaging Techniques and Applications #Computational Geometry (cs.CG) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Morphological variations and asymmetry #Topological and Geometric Data Analysis

paper · pdf · doi:10.48550/arxiv.2206.04189

openalex publication_date 2022/06/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Most dimensionality reduction methods employ frequency domain representations obtained from matrix diagonalization and may not be efficient for large datasets with relatively high intrinsic dimensions. To address this challenge, Correlated Clustering and Projection (CCP) offers a novel data domain strategy that does not need to solve any matrix. CCP partitions high-dimensional features into correlated clusters and then projects correlated features in each cluster into a one-dimensional representation based on sample correlations. Residue-Similarity (R-S) scores and indexes, the shape of data in Riemannian manifolds, and algebraic topology-based persistent Laplacian are introduced for visualization and analysis. Proposed methods are validated with benchmark datasets associated with various machine learning algorithms.

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