Incremental multi-view spectral clustering

Abstract

Multi-view learning has attracted increasing attention in recent years, and the existing multi-view learning methods learn a consensus result by collecting all views. These methods have two obvious limitations. First, it is not scalable; with limited computational resources it would be difficult, if not impossible, to collect and process a large collection of views together. Second, in many applications views of data are available over time; it is infeasible to apply the existing multi-view learning methods to such streaming views. To address the two limitations, in this paper we propose a novel incremental multi-view spectral clustering (IMSC) method. In IMSC, instead of ensembling the collection of all views simultaneously, we integrate them one by one in an incremental way. We first learn an initial model from a small number of views; next when a new view is available, we need only use it to update the model and apply the updated model to learn a consensus result. This method is scalable and applicable to streaming views. To further reduce the time and space complexity, we apply low rank approximation by means of the well-known random Fourier features to construct the base kernels and do low rank SVD decompositions accordingly. The theoretical analysis and experimental results on benchmark data sets show that our incremental multi-view spectral clustering method is significantly faster in efficiency than the existing state-of-the-art non-incremental ones and is comparable or even better in clustering quality.