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Adaptive bigraph-based multi-view unsupervised dimensionality reduction.

Qianyao Qiang¹, Bin Zhang², Chen Jason Zhang¹

¹Department of Computing, Hong Kong Polytechnic University, 999077, Hong Kong, China.

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

|March 30, 2025

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This study introduces adaptive Bigraph-based Multi-view Unsupervised Dimensionality Reduction (BMUDR) for unlabeled data. BMUDR enhances representation learning by adaptively constructing graphs and weighting diverse data views for improved efficiency and performance.

Area of Science:

Machine Learning
Data Science
Computer Vision

Background:

Graph-based multi-view unsupervised dimensionality reduction is vital for unlabeled data.
Existing methods struggle with heterogeneous data integration, rigid graphs, and computational demands.

Purpose of the Study:

To propose an adaptive Bigraph-based Multi-view Unsupervised Dimensionality Reduction (BMUDR) method.
To address challenges in integrating multiple views and learning low-dimensional representations.

Main Methods:

BMUDR dynamically learns view-specific anchor sets and constructs an adaptive bigraph.
It integrates anchor generation and similarity matrix construction into dimensionality reduction.
An optimization algorithm enhances computational efficiency and scalability.

Keywords:

Adaptive graph Bipartite graph Embedding Multi-view dimensionality reduction Unsupervised learning

Related Experiment Videos

Main Results:

BMUDR effectively learns low-dimensional representations by exploring sample-anchor relationships.
The method adaptively weighs diverse view contributions, leveraging complementary and consistent properties.
Extensive experiments demonstrate impressive performance on benchmark datasets.

Conclusions:

BMUDR offers a robust solution for multi-view unsupervised dimensionality reduction.
The adaptive bigraph approach improves the integration and representation of heterogeneous data.
The method shows significant potential for enhancing machine learning tasks involving unlabeled multi-view data.