A random walk based multi-kernel graph learning framework

Graph learning is an important approach for machine learning. Kernel method is efficient for constructing similarity graph. Single kernel isn't sufficient for complex problems. In this paper we propose a framework for multi-kernel learning. We give a brief introduction of Gaussian kernel, LLE and sparse representation. Then we analyze the advantages and disadvantages of these methods and give the reason why the combine of these methods with random walk is efficient. We compare our method with baseline methods on real-world data sets. The results show the efficiency of our method.