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In multi-view clustering, datasets are comprised of different representations of the data, or views. Although each view could individually be used, exploiting information from all views together could improve the cluster quality. In this paper a new model Multi-View Kernel Spectral Clustering (MVKSC) is proposed that performs clustering when two or more views are available. This model is formulated as a weighted kernel canonical correlation analysis in a primal-dual optimization setting typical of Least Squares Support Vector Machines (LS-SVM). The primal model includes, in particular, a coupling term, which enforces the clustering scores corresponding to the different views to align. Because of the out-of-sample extension, this model is easily applied to large-scale datasets. The performance of the proposed model is shown on synthetic and real-world datasets, as well as on some large-scale datasets. Experimental comparisons with a number of other methods show that using multiple views improves the clustering results and that the proposed method is competitive with other state-of-the-art algorithms in terms of clustering accuracy and runtime. Especially on the large-scale datasets the advantage of the proposed method is clearly shown, as it is able to handle larger datasets than the other state-of-the-art algorithms.

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In many real-life applications data can be described through multiple representations, or views. Multi-view learning aims at combining the information from all views, in order to obtain a better performance. Most well-known multi-view methods optimize some form of correlation between two views, while in many applications there are three or more views available. This is usually tackled by optimizing the correlations pairwise. However, this ignores the higher-order correlations that could only be discovered when exploring all views simultaneously. This paper proposes novel multi-view Kernel PCA models. By introducing a model tensor, the proposed models aim to include the higher-order correlations between all views. The paper further explores the use of these models as multi-view dimensionality reduction techniques and shows experimental results on several real-life datasets. These experiments demonstrate the merit of the proposed methods.