Kernel methods provide a convenient way to apply a wide range of learning techniques to complex and structured data by shifting the representational problem from one of finding an embedding of the data to that of defining a positive semidefinite kernel. One problem with the most widely used kernels is that they neglect the locational information within the structures, resulting in less discrimination. Correspondence-based kernels, on the other hand, are in general more discriminating, at the cost of sacrificing positive-definiteness due to their inability to guarantee transitivity of the correspondences between multiple graphs. In this paper we generalize a recent structural kernel based on the Jensen-Shannon divergence between quantum walks over the structures by introducing a novel alignment step which rather than permuting the nodes of the structures, aligns the quantum states of their walks. This results in a novel kernel that maintains localization within the structures, but still guarantees positive definiteness. Experimental evaluation validates the effectiveness of the kernel for several structural classification tasks. © 2014 Springer-Verlag Berlin Heidelberg.
|Data di pubblicazione:||2014|
|Titolo:||Transitive State Alignment for the Quantum Jensen-Shannon KernelStructural, Syntactic, and Statistical Pattern Recognition|
|Titolo del libro:||Lecture Notes in Computer ScienceStructural, Syntactic, and Statistical Pattern Recognition|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/978-3-662-44415-3_3|
|Appare nelle tipologie:||4.1 Articolo in Atti di convegno|