Third assignment for A.I. course, Prof. Torsello, Ca' Foscari University of Venice, A.Y. 2018/2019
Read this article presenting a way to improve the discriminative power of graph kernels.
Choose one graph kernel among
- Shortest-path Kernel
- Graphlet Kernel
- Random Walk Kernel
- Weisfeiler-Lehman Kernel
Choose one manifold learning technique among
- Isomap
- Diffusion Maps
- Laplacian Eigenmaps
- Local Linear Embedding
Compare the performance of an SVM trained on the given kernel, with or without the manifold learning step, on the following datasets:
Note: the datasets are contained in Matlab files. The variable G contains a vector of cells, one per graph.
The entry am of each cell is the adjacency matrix of the graph.
The variable labels contains the class-labels of each graph.