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Updated: Jun 28, 2025

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

Self-paced regularized adaptive multi-view unsupervised feature selection.

Xuanhao Yang¹, Hangjun Che², Man-Fai Leung³

¹College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China.

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

|April 13, 2024

Summary

This summary is machine-generated.

This study introduces Self-paced Regularized Adaptive Multi-view Unsupervised Feature Selection (SPAMUFS) to improve feature selection for complex, heterogeneous data by adaptively weighting samples and views. SPAMUFS enhances dimensional reduction by better utilizing sample diversity and preserving data structure.

Keywords:

Hypergraph Multi-view unsupervised feature selection Self-paced learning Sparse learning l(2,p)-norm

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Area of Science:

Machine Learning
Data Science
Computer Vision

Background:

Multi-view unsupervised feature selection (MUFS) is crucial for reducing dimensionality in heterogeneous data.
Existing MUFS methods often overlook sample diversity and struggle with non-convex optimization problems.
This leads to suboptimal feature selection and inefficient data representation.

Purpose of the Study:

To propose a novel MUFS method, Self-paced Regularized Adaptive Multi-view Unsupervised Feature Selection (SPAMUFS), that addresses limitations of existing approaches.
To enhance the utilization of sample diversity and improve the handling of non-convexity in MUFS.
To develop a robust feature selection technique for multi-view heterogeneous data.

Main Methods:

SPAMUFS employs a self-paced learning strategy to progressively train the model with increasingly complex samples.
It utilizes the l2,p-norm for measuring learning error and enforcing sparsity, accommodating diverse dataset requirements.
Hypergraph Laplacian matrices are constructed for each view to capture local manifold structures and high-order relationships, with adaptive view weighting.

Main Results:

The proposed iterative optimization algorithm effectively solves the SPAMUFS problem, with analyzed convergence and computational complexity.
SPAMUFS demonstrated superior performance compared to eight state-of-the-art algorithms across nine public multi-view datasets.
The method effectively leverages sample diversity and inter-view correlations for improved feature selection.

Conclusions:

SPAMUFS offers a significant advancement in multi-view unsupervised feature selection by effectively handling sample diversity and complex data structures.
The self-paced learning and adaptive weighting mechanisms contribute to more robust and efficient dimensional reduction.
The proposed method provides a promising solution for various applications involving multi-view heterogeneous data analysis.