Fast Multiview Anchor-Graph Clustering

Ben Yang

doi:10.1109/TNNLS.2024.3359690

Fast Multiview Anchor-Graph Clustering

2025-03-01

3

PRE

AI

DOI：10.1109/TNNLS.2024.3359690

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Due to its high computational complexity, graph-based methods have limited applicability in large-scale multiview clustering tasks. To address this issue, many accelerated algorithms, especially anchor graph-based methods and indicator learning-based methods, have been developed and made a great success. Nevertheless, since the restrictions of the optimization strategy, these accelerated methods still need to approximate the discrete graph-cutting problem to a continuous spectral embedding problem and utilize different discretization strategies to obtain discrete sample categories. To avoid the loss of effectiveness and efficiency caused by the approximation and discretization, we establish a discrete fast multiview anchor graph clustering (FMAGC) model that first constructs an anchor graph of each view and then generates a discrete cluster indicator matrix by solving the discrete multiview graph-cutting problem directly. Since the gradient descent-based method makes it hard to solve this discrete model, we propose a fast coordinate descent-based optimization strategy with linear complexity to solve it without approximating it as a continuous one. Extensive experiments on widely used normal and large-scale multiview datasets show that FMAGC can improve clustering effectiveness and efficiency compared to other state-of-the-art baselines.

Keyword:

Learning systems

Optimization

Clustering algorithms

Computational complexity

Tensors

Standards

Computational modeling

Anchor graph

fast clustering

large-scale data

multiview data

Fast Multiview Anchor-Graph Clustering

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