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Two-stage multiple kernel learning (MKL) algorithms have been extensively researched in recent years due to their high efficiency and effectiveness. Previous works have attempted to optimize the combination coefficients by maximizing the centralized kernel alignment between the combined kernel and the ideal kernel. Though demonstrating previous promising performance, we observe that these algorithms...
We propose a fast algorithm for approximate matching of large graphs. Previous graph matching algorithms suffer from high computational complexity and therefore do not have good scalability. By using a new doubly stochastic projection, for matching two weighted graphs of n nodes, our algorithm has time complexity only O(n3) per iteration and space complexity O(n2). We proved that our algorithm converges...
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