Large-scale Multi-view Subspace Clustering in Linear Time
This addresses the scalability problem for researchers and practitioners dealing with big data in clustering tasks, though it is incremental as it builds on existing anchor graph ideas.
The paper tackles the computational inefficiency of multi-view subspace clustering (MVSC) methods, which often have quadratic or cubic complexity, by proposing a large-scale MVSC algorithm with linear complexity, validated through extensive experiments on benchmark datasets.
A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years. Researchers manage to boost clustering accuracy from different points of view. However, many state-of-the-art MVSC algorithms, typically have a quadratic or even cubic complexity, are inefficient and inherently difficult to apply at large scales. In the era of big data, the computational issue becomes critical. To fill this gap, we propose a large-scale MVSC (LMVSC) algorithm with linear order complexity. Inspired by the idea of anchor graph, we first learn a smaller graph for each view. Then, a novel approach is designed to integrate those graphs so that we can implement spectral clustering on a smaller graph. Interestingly, it turns out that our model also applies to single-view scenario. Extensive experiments on various large-scale benchmark data sets validate the effectiveness and efficiency of our approach with respect to state-of-the-art clustering methods.