In this presentation, we explore a cutting-edge method in multi-view graph learning, an approach that innovatively integrates weight learning with graph learning within an alternating optimization framework. This technique, pivotal in scenarios lacking prior data distribution knowledge, constructs a unified affinity graph from diverse data representation sources. Our unique fusion-and-diffusion strategy amalgamates multiple affinity graphs through a weight learning scheme, which is developed from unsupervised graph smoothness. These fused graphs then serve as a consensus foundation for further diffusion.
The cornerstone of our methodology is a novel multi-view diffusion process. It innovatively learns a manifold-aware affinity graph by propagating affinities across tensor product graphs. This process not only captures high-order contextual information but also significantly enhances pairwise affinities. A key advantage of our approach is its independence from the limitations that typically constrain existing multi-view graph learning methods, such as the reliance on the quality of initial graphs or the presupposition of a latent common subspace among multiple views. Our method adeptly identifies consistencies across views and adaptively fuses multiple graphs.
We present a unified framework that encompasses both weight learning and diffusion-based affinity learning, accompanied by an alternating optimization solver with a convergence guarantee. This novel approach has been applied to image retrieval and clustering tasks across 16 real-world datasets. Our extensive experiments reveal that this method surpasses current leading techniques in both retrieval and clustering in 13 out of these 16 datasets.