Deep Weisfeiler-Lehman Assignment Kernels via Multiple Kernel Learning
This work addresses graph classification by improving kernel methods, but it appears incremental as it builds on existing assignment kernel theory with a new learning approach.
The paper tackled the problem of learning base kernels for structured data classification by optimizing hierarchy weights via multiple kernel learning, specifically for the Weisfeiler-Lehman optimal assignment kernel in graph classification, and demonstrated feasibility and effectiveness in initial experiments.
Kernels for structured data are commonly obtained by decomposing objects into their parts and adding up the similarities between all pairs of parts measured by a base kernel. Assignment kernels are based on an optimal bijection between the parts and have proven to be an effective alternative to the established convolution kernels. We explore how the base kernel can be learned as part of the classification problem. We build on the theory of valid assignment kernels derived from hierarchies defined on the parts. We show that the weights of this hierarchy can be optimized via multiple kernel learning. We apply this result to learn vertex similarities for the Weisfeiler-Lehman optimal assignment kernel for graph classification. We present first experimental results which demonstrate the feasibility and effectiveness of the approach.