Beyond Euclidean Structures: Collaborative Topological Graph Learning for Multiview Clustering.

Area of Science:

• Machine Learning
• Data Mining
• Computer Vision

Background:

• Graph-based multiview clustering (MVC) methods excel by using data consistency but often rely on fixed graph structures.
• Existing approaches may fail to capture the true consensus topology in multiview data, limiting clustering performance.
• There's a need for adaptive methods to explore consensus topological structures for improved intrinsic graph learning.

Purpose of the Study:

• To propose a novel approach, collaborative topological graph learning (CTGL), for enhancing multiview clustering.
• To adaptively discover and leverage consistent topological structures to guide intrinsic graph learning.
• To improve the accuracy of clustering outcomes in multiview settings.

Main Methods:

• Introduced CTGL, which adaptively discovers consistent topological structures to guide intrinsic graph learning.
• Developed an auxiliary consistency graph to formulate the topological relevance learning function.
• Employed a collaborative learning strategy using tensor learning to simultaneously learn auxiliary and view-specific graphs.

Main Results:

• CTGL adaptively explores consensus topological structures, leading to more accurate clustering.
• The collaborative learning strategy effectively overcomes challenges in estimating the auxiliary consistency graph.
• Extensive experiments demonstrate the superior effectiveness of the proposed CTGL method.

Conclusions:

• CTGL offers a significant advancement in multiview clustering by adaptively learning topological structures.
• The proposed collaborative learning strategy enhances graph learning accuracy and clustering performance.
• The method provides a robust solution for uncovering consensus topology in multiview data.