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  1. Home
  3. Federated Multi-view Unsupervised Feature Selection Via Bio-inspired Hierarchical-cognitive Tianji's Horse Racing Optimization And Tensor Learning.
  1. Home
  3. Federated Multi-view Unsupervised Feature Selection Via Bio-inspired Hierarchical-cognitive Tianji's Horse Racing Optimization And Tensor Learning.

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Related Experiment Video

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Federated Multi-View Unsupervised Feature Selection via Bio-Inspired Hierarchical-Cognitive Tianji's Horse Racing

Rong Cheng1, Zhiwei Sun2, Kun Qi1

  • 1School of Electronic and Communication Engineering, Shenzhen Polytechnic University, Shenzhen 518055, China.

Biomimetics (Basel, Switzerland)
|May 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces Fed-MUFSHT, a federated learning framework for multi-view unsupervised feature selection (MUFS). It enhances optimization and convergence for privacy-preserving distributed machine learning tasks.

Keywords:
Tianji’s horse racing optimizationbio-inspired computationfederated learningmulti-view unsupervised feature selectionprivacy preservationtensor learning

Related Experiment Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Multi-view datasets are prevalent but often unlabeled and distributed, challenging traditional centralized machine learning.
  • Existing federated and centralized methods struggle with local optima and convergence guarantees in federated multi-view feature selection.
  • Privacy concerns and communication constraints hinder the application of centralized methods to distributed multi-view data.

Purpose of the Study:

  • To propose Fed-MUFSHT, a federated framework for multi-view unsupervised feature selection (MUFS).
  • To address challenges of privacy, communication, local optima, and convergence in distributed multi-view data.
  • To enhance feature selection performance and robustness in federated learning settings.

Main Methods:

  • Fed-MUFSHT integrates tensor learning (TL) with a novel metaheuristic optimizer, Hierarchical-Cognitive Tianji's Horse Racing Optimization (HC-THRO).
  • A dual-stage local optimization process includes HC-THRO (Hierarchical Competitive Learning and Adaptive Cognitive Mapping) for exploration and Stage 2 TL for imputation and representation.
  • Global model coordination uses a privacy-preserving aggregation strategy based on Normalized Mutual Information (NMI) and feature weights.

Main Results:

  • Fed-MUFSHT demonstrated clear advantages over competing methods on benchmark datasets.
  • The proposed framework achieved better optimization results and more dependable convergence characteristics.
  • Experiments confirmed the robustness and effectiveness of Fed-MUFSHT for distributed optimization with privacy protection.

Conclusions:

  • Fed-MUFSHT offers a robust and effective solution for multi-view unsupervised feature selection in federated learning.
  • The integration of tensor learning and HC-THRO significantly improves optimization and convergence.
  • The framework successfully addresses privacy and communication constraints in distributed multi-view data analysis.