CCP: Correlated Clustering and Projection for Dimensionality Reduction

• URL: http://arxiv.org/abs/2206.04189v1
• Date: Wed, 8 Jun 2022 23:14:44 GMT
• Title: CCP: Correlated Clustering and Projection for Dimensionality Reduction
• Authors: Yuta Hozumi, Rui Wang, Guo-Wei Wei
• Abstract summary: Correlated Clustering and Projection 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. Proposed methods are validated with benchmark datasets associated with various machine learning algorithms.
• Score: 5.992724190105578
• License: http://creativecommons.org/licenses/by/4.0/
• 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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