Nonnegative and Adaptive Multi-view Clustering

Abstract

This paper proposes a novel Nonnegative and Adaptive Multi-view Clustering (NAMC) method. NAMC integrates the nonnegative matrix factorization (NMF), adaptive neighborhood learning and consensus adaptive similarity matrix fusion. More specifically, NAMC performs the nonnegative weight learning over the original data and the parts-based representations of NMF for more accurate measure and representation. For nonnegative adaptive feature extraction, our model first utilizes NMF to obtain the local parts-based representation of the original high-dimensional data. To keep the local structure of parts-based representations, we minimize the adaptive neighborhood reconstruction error. Then the optimal consensus similarity matrix can be iteratively obtained according to the nonnegative adaptive similarity matrix of each view. What's more, the proposed NAMC is totally self-weighted. Once the target graph is obtained in our model, it can be partitioned into specific clusters directly. Extensive simulations show that NAMC can achieve good performance on several public databases for multi-view clustering, compared with other related methods.