Multi-view subspace clustering via partition fusion

Fuzzy graph clustering

Multi-view spectral clustering via constrained nonnegative embedding

Recently, multi-view clustering has received much attention in the fields of machine learning and pattern recognition. Spectral clustering for single and multiple views has been the common solution. Despite its good clustering performance, it has a major limitation: it requires an extra step of clustering. This extra step, which could be the famous k-means clustering, depends heavily on initialization, which may affect the quality of the clustering result. To overcome this problem, a new method called Multi-view Clustering via Consensus Graph Learning and Nonnegative Embedding (MVCGE) is presented in this paper. In the proposed approach, the consensus affinity matrix (graph matrix), consensus representation and cluster index matrix (nonnegative embedding) are learned simultaneously in a unified framework. Our proposed method takes as input the different kernel matrices corresponding to the different views. The proposed learning model integrates two interesting constraints: (i) the cluster indices should be as smooth as possible over the consensus graph and (ii) the cluster indices are set to be as close as possible to the graph convolution of the consensus representation. In this approach, no post-processing such as k-means or spectral rotation is required. Our approach is tested with real and synthetic datasets. The experiments performed show that the proposed method performs well compared to many state-of-the-art approaches. 

Consensus graph and spectral representation for one-step multi-view kernel based clustering

Simultaneous learning coefficient matrix and affinity graph for multiple kernel clustering

Multiple kernel clustering with pure graph learning scheme

Image set classification has drawn much attention due to its promising performance to overcome various variations. Recently, the point-to-point distance-based methods have achieved the state-of-the-art performance by leveraging the distance between the gallery set and the probe set. However, there are two drawbacks that need to be defeated: 1) they do not fully exploit the discrimination that exists between different gallery sets; 2) they face the great challenge of high computational complexity as well as multi-parameters, usually caused by some obvious sparse constraints. To address these problems, this paper proposes a novel method, namely latent sparse discriminative learning (LSDL), for face image set classification. Specifically, a new term is proposed to exploit the relations between different gallery sets, which can boost the set discrimination so as to improve performance. Moreover, we use a latent sparse constraint to reduce the trade-off parameters and computational cost. Furthermore, an efficient solver is proposed to solve our LSDL. Experimental results on three benchmark datasets demonstrate the advantages of our propose.

Haoyun Lei

Papers

Simultaneous learning coefficient matrix and affinity graph for multiple kernel clustering

Multiple kernel clustering with pure graph learning scheme

Latent Sparse Discriminative Learning for Face Image Set Classification