A review on multi-view learning

Abstract

Multi-view learning is an emerging field that aims to enhance learning performance by leveraging multiple views or sources of data across various domains. By integrating information from diverse perspectives, multi-view learning methods effectively enhance accuracy, robustness, and generalization capabilities. The existing research on multi-view learning can be broadly categorized into four groups in the survey based on the tasks it encompasses, namely multi-view classification approaches, multi-view semi-supervised classification approaches, multi-view clustering approaches, and multi-view semi-supervised clustering approaches. Despite its potential advantages, multi-view learning poses several challenges, including view inconsistency, view complementarity, optimal view fusion, the curse of dimensionality, scalability, limited labels, and generalization across domains. Nevertheless, these challenges have not discouraged researchers from exploring the potential of multi-view learning. It continues to be an active and promising research area, capable of effectively addressing complex real-world problems.

Graphical abstract

Keywords

multi-view learning / multi-view clustering / ensemble learning / semi-supervised learning

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The Author(s) 2024. This article is published with open access at link.springer.com and journal.hep.com.cn