Adaptive bigraph-based multi-view unsupervised dimensionality reduction

Abstract

As a crucial machine learning technology, graph-based multi-view unsupervised dimensionality reduction aims to learn compact low-dimensional representations for unlabeled multi-view data using graph structures. However, it faces several challenges, including the integration of multiple heterogeneous views, the absence of label guidance, the rigidity of predefined similarity graphs, and high computational intensity. To address these issues, we propose a novel method called adaptive Bigraph-based Multi-view Unsupervised Dimensionality Reduction (BMUDR). BMUDR dynamically learns view-specific anchor sets and adaptively constructs a bigraph shared by multiple views, facilitating the discovery of low-dimensional representations through sample-anchor relationships. The generation of anchors and the construction of anchor similarity matrices are integrated into the dimensionality reduction process. Diverse contributions of different views are automatically weighed to leverage their complementary and consistent properties. In addition, an optimization algorithm is designed to enhance computational efficiency and scalability, and it provides impressive performance in low-dimensional representation learning, as demonstrated by extensive experiments on various benchmark datasets.