Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Pattern Vectors from Algebraic Graph Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering
International Journal of Computer Vision
A Riemannian approach to graph embedding
Pattern Recognition
A spectral approach to learning structural variations in graphs
Pattern Recognition
Graph embedding using tree edit-union
Pattern Recognition
Bayesian optimization of the scale saliency filter
Image and Vision Computing
Exact Median Graph Computation Via Graph Embedding
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Polytopal Graph Complexity, Matrix Permanents, and Embedding
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Graph characteristics from the heat kernel trace
Pattern Recognition
On the relation between the median and the maximum common subgraph of a set of graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Graph embedding in vector spaces by means of prototype selection
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
What is the complexity of a network? the heat flow-thermodynamic depth approach
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Graph Kernels from the Jensen-Shannon Divergence
Journal of Mathematical Imaging and Vision
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In this paper we show how to approximate the von Neumann entropy associated with the Laplacian eigenspectrum of graphs and exploit it as a characteristic for the clustering and classification of graphs. We commence from the von Neumann entropy and approximate it by replacing the Shannon entropy by its quadratic counterpart. We then show how the quadratic entropy can be expressed in terms of a series of permutation invariant traces. This leads to a simple approximate form for the entropy in terms of the elements of the adjacency matrix which can be evaluated in quadratic time. We use this approximate expression for the entropy as a unary characteristic for graph clustering. Experiments on real world data illustrate the effectiveness of the method.