@inproceedings{89da2ebf2d5a4425b506e6f0fd4e5c23,
title = "Manifold learning and the quantum Jensen-Shannon divergence kernel",
abstract = "The quantum Jensen-Shannon divergence kernel [1] was recently introduced in the context of unattributed graphs where it was shown to outperform several commonly used alternatives. In this paper, we study the separability properties of this kernel and we propose a way to compute a low-dimensional kernel embedding where the separation of the different classes is enhanced. The idea stems from the observation that the multidimensional scaling embeddings on this kernel show a strong horseshoe shape distribution, a pattern which is known to arise when long range distances are not estimated accurately. Here we propose to use Isomap to embed the graphs using only local distance information onto a new vectorial space with a higher class separability. The experimental evaluation shows the effectiveness of the proposed approach.",
keywords = "Continuous-Time Quantum Walk, Graph Kernels, Manifold Learning, Quantum Jensen-Shannon Divergence",
author = "Luca Rossi and Andrea Torsello and Hancock, {Edwin R.}",
year = "2013",
month = aug,
doi = "10.1007/978-3-642-40261-6_7",
language = "English",
isbn = "9783642402609",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 1",
pages = "62--69",
booktitle = "Computer Analysis of Images and Patterns - 15th International Conference, CAIP 2013, Proceedings",
edition = "PART 1",
note = "15th International Conference on Computer Analysis of Images and Patterns, CAIP 2013 ; Conference date: 27-08-2013 Through 29-08-2013",
}