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Multi-kernel clustering with tensor fusion on Grassmann manifold for high-dimensional genomic data.

Fei Qi1, Jin Guo2, Junyu Li3

  • 1Data Science and Information Engineering, Guizhou Minzu University, Guiyang, 550025, Guizhou, China; Computer Science and Engineering, South China University of Technology, Guangzhou, 510006, Guangdong, China.

Methods (San Diego, Calif.)
|October 13, 2024
PubMed
Summary

This study introduces a novel multi-kernel clustering method (MKCTM) using tensor fusion on Grassmann manifolds to effectively handle high-dimensional genomic data. MKCTM enhances clustering accuracy by reducing noise and redundancy in base kernels.

Keywords:
Grassmann manifoldHigh-dimensional clusteringTensor representation

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Area of Science:

  • Bioinformatics
  • Machine Learning
  • Data Science

Background:

  • High-dimensional genomic data presents significant challenges for traditional clustering algorithms due to noise and complexity.
  • Existing multi-kernel clustering methods improve affinity matrix quality but struggle with error propagation and redundancy in high-dimensional settings.
  • Current multi-kernel fusion strategies lack feasibility for effectively integrating diverse kernel information.

Purpose of the Study:

  • To develop an advanced multi-kernel clustering method capable of overcoming the limitations of existing approaches in high-dimensional genomic data analysis.
  • To introduce a novel tensor fusion strategy on Grassmann manifolds for robust kernel integration.
  • To enhance clustering performance by maximizing consensus among base kernels while mitigating noise and redundancy.

Main Methods:

  • Proposed a Multi-Kernel Clustering method with Tensor fusion on Grassmann manifolds (MKCTM).
  • Employed tensor low-rank constraints to maximize clustering consensus and eliminate noise/redundancy from base kernels.
  • Developed a unified optimization model integrating tensor learning and fusion, solved by an effective iterative algorithm.
  • Fused learned base kernels on the Grassmann manifold to generate a final consensus matrix for clustering.

Main Results:

  • MKCTM demonstrated superior performance compared to 12 popular baseline clustering methods across ten diverse datasets.
  • The tensor fusion approach effectively reduced noise and redundancy, leading to improved clustering accuracy.
  • The method successfully integrated information from multiple base kernels through Grassmann manifold fusion.

Conclusions:

  • MKCTM offers a robust and effective solution for clustering high-dimensional genomic data, outperforming existing methods.
  • The proposed tensor fusion strategy on Grassmann manifolds is a significant advancement in multi-kernel learning.
  • The developed iterative optimization algorithm efficiently solves the integrated tensor learning and fusion model.