An Empirical Study on Budget-Aware Online Kernel Algorithms for Streams of Graphs
This work addresses memory-efficient online learning for graph streams, which is an incremental improvement tailored to a specific domain.
The study tackled the problem of online kernel learning for graph streams under memory constraints by comparing feature space approaches to dual methods, finding that feature space methods are more effective in terms of speed and classification performance when strict budgets are enforced.
Kernel methods are considered an effective technique for on-line learning. Many approaches have been developed for compactly representing the dual solution of a kernel method when the problem imposes memory constraints. However, in literature no work is specifically tailored to streams of graphs. Motivated by the fact that the size of the feature space representation of many state-of-the-art graph kernels is relatively small and thus it is explicitly computable, we study whether executing kernel algorithms in the feature space can be more effective than the classical dual approach. We study three different algorithms and various strategies for managing the budget. Efficiency and efficacy of the proposed approaches are experimentally assessed on relatively large graph streams exhibiting concept drift. It turns out that, when strict memory budget constraints have to be enforced, working in feature space, given the current state of the art on graph kernels, is more than a viable alternative to dual approaches, both in terms of speed and classification performance.