ArticleMachine learning: science and technology2026
Correlated clustering and projection for dimensionality reduction.
Article in Machine learning: science and technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Analyzing Single Cell RNA Sequencing with Topological Nonnegative Matrix Factorization.Journal of computational and applied mathematics · 2024Article
- K-nearest-neighbors induced topological PCA for single cell RNA-sequence data analysis.Computers in biology and medicine · 2024Article
- Preprocessing of Single Cell RNA Sequencing Data Using Correlated Clustering and Projection.Journal of chemical information and modeling · 2024Article
- PLPCA: Persistent Laplacian-Enhanced PCA for Microarray Data Analysis.Journal of chemical information and modeling · 2024Article
- Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data.Computers in biology and medicine · 2024Article
- Analyzing scRNA-seq data by CCP-assisted UMAP and tSNE.PloS one · 2024Article
- Article
- Machine Learning Methods for Small Data Challenges in Molecular Science.Chemical reviews · 2023Review
- SVSBI: sequence-based virtual screening of biomolecular interactions.Communications biology · 2023Article
- Integrating transformer and autoencoder techniques with spectral graph algorithms for the prediction of scarcely labeled molecular data.Computers in biology and medicine · 2023Article
- Virtual screening of DrugBank database for hERG blockers using topological Laplacian-assisted AI models.Computers in biology and medicine · 2023Article
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Authors and funding
3 authors.
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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 using benchmark datasets associated with various machine learning algorithms.
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