Multiple Graph Regularized Concept Factorization With Adaptive Weights

Many traditional concept factorization methods employ single graph to approximate the manifold structure of data. Therefore, they cannot capture the underlying geometric structure hidden in data effectively. In this paper, we propose a novel method, called Multiple graph regularized Concept Factorization with Adaptive Weights (MCFAWs), for data representation. It exploits the intrinsic geometric manifold of the data by the linear combination of multiple graphs with parameter free. Therefore, our proposed MCFAW method can be applied to many real problems. Besides, an efficient optimization algorithm is presented to solve the proposed model. Some experimental results on the benchmarks show that the proposed MCFAW method outperforms the state-of-the-art methods.