Centroids-guided deep multi-view K-means clustering

Abstract

With the progress of deep learning used in unsupervised learning, deep approach based multi-view clustering methods have been increasingly proposed in recent years. However, in most of these methods, deep representation learning is not organically integrated into the multi-view clustering process. They either conduct deep representation learning and clustering in a separate manner, or use the pseudo cluster labels to supervise deep representation learning. In this paper, we propose a centroids-guided deep multi-view k-means clustering method, which organically incorporates deep representation learning into the multi-view k-means objective by using the cluster centroids in multi-view k-means to guide the deep learning of each view. In turn, more k-means-friendly representations are produced to further optimize the multi-view k-means objective. The cluster centroids of each view obtained under a common clustering partition not only represent the semantic information of the clusters but also imply consistency among different views. By reducing the loss between each representation and its assigned cluster centroid with respect to the network parameters of each view, the representations of different views will be more k-means-friendly toward a common partition. Experiments on several datasets demonstrate the effectiveness of our method.