Multiview Clustering: A Scalable and Parameter-Free Bipartite Graph Fusion Method.

Area of Science:

• Machine Learning
• Data Mining
• Computer Science

Background:

• Multiview clustering groups data based on diverse features.
• Existing methods often struggle with intractable hyper-parameters and high computational costs.
• Traditional spectral methods face challenges with explicit cluster exploration and time efficiency.

Purpose of the Study:

• To develop a scalable and parameter-free graph fusion framework for multiview clustering.
• To overcome limitations of existing methods regarding hyper-parameters and computational complexity.
• To enable direct cluster identification from fused graph representations.

Main Methods:

• A novel graph fusion framework that interactively learns view weights and a joint graph.
• A self-supervised weighting mechanism to coalesce multiple view-wise graphs.
• A connectivity constraint on the joint graph for direct cluster extraction.

Main Results:

• The proposed method is parameter-free, eliminating the need for weight-related hyper-parameter tuning.
• The algorithm is initialization-independent and time-economical, demonstrating scalability with data size.
• Experiments show superior clustering performance and reduced time expenditure compared to state-of-the-art methods.

Conclusions:

• The developed framework offers an efficient and effective solution for multiview clustering.
• It successfully addresses the challenges of hyper-parameter tuning and computational cost.
• The parameter-free, scalable approach provides stable performance on various datasets.