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Related Experiment Video

Updated: Jan 8, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Robust structure-preservation tensorized representation for multi-view unsupervised feature selection.

Chen Wang¹, Peng Song¹, Changjia Wang¹

¹School of Computer and Control Engineering, Yantai University, Yantai, 264005, China.

Neural Networks : the Official Journal of the International Neural Network Society

|December 23, 2025

Summary

This summary is machine-generated.

This study introduces a robust structure-preservation tensorized representation (RSTR) for multi-view unsupervised feature selection (MUFS). RSTR effectively handles noisy data and optimizes view structures, outperforming existing methods.

Keywords:

Feature selection Multi-view learning Rank constraint Scaled simplex representation Tensor

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Area of Science:

Machine Learning
Data Mining
Computer Science

Background:

Self-representation methods construct affinity matrices for multi-view unsupervised feature selection (MUFS).
Existing methods are challenged by data noise, distorted relationships due to constraints, and suboptimal view structure exploitation.

Purpose of the Study:

To propose a novel robust structure-preservation tensorized representation (RSTR) method for MUFS.
To address limitations of existing methods in handling noise and optimizing view structures.

Main Methods:

RSTR decomposes affinity matrices into clean and noise components to reduce noise impact.
Introduces scaled simplex constraint for physically meaningful affinity matrices and inherent structure discovery.
Employs rank constraint to identify optimal view structures and constructs a weighted tensor to mine high-order relationships across views.

Main Results:

The proposed RSTR method demonstrates superior performance compared to state-of-the-art competitors.
Experiments on eight datasets validate the effectiveness of RSTR in improving MUFS.

Conclusions:

RSTR offers a robust and effective approach for multi-view unsupervised feature selection.
The method successfully mitigates noise, preserves inherent data structures, and optimizes view representations.