Centroids-guided deep multi-view K-means clustering

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 inte-grated 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 rep-resentations are produced to further optimize the multi-view k-means objective. The clus-ter centroids of each view obtained under a common clustering partition not only represent the semantic information of the clusters but also imply consistency among dif-ferent 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 dif-ferent views will be more k-means-friendly toward a common partition. Experiments on several datasets demonstrate the effectiveness of our method.(c) 2022 Elsevier Inc. All rights reserved.