Unpaired multi-view kernel spectral clustering
Lynn Houthuys, Johan A. K. Suykens · 2017
In multi-view learning, data is described through multiple representations or views. Multi-view learning methods aim to improve the performance of using only one view, by incorporating the information of all available views. A common assumption is that the data is paired, which could be over-rigorous in certain applications. This paper introduces an unpaired multi-view clustering model called Unpaired Multi-View Kernel Spectral Clustering (UPMVKSC) which performs multi-view clustering when there is no information about which points in the different views represent the same object. The information that is included, is in the form of pairwise inter-and intra-view constraints. The proposed model is tested on four different datasets and the experimental results demonstrate the effectiveness of our model and the behavior with respect to the number of constraints used.