In our experiments we show that all of these divergences are highly discriminative: all of them improve the retrieval-recall results obtained with the Henze- ...
The accurate and effective measurement of graph- similarity has proved to be a challenging problem in struc- tural pattern recognition.
This paper extends the node coverage approach for graph indexing by replacing the Henze-Penrose divergence by a Total Bregman Divergence (TBD) which relies ...
Tensor-based total bregman divergences between graphs · Computer Science, Mathematics. IEEE International Conference on Computer Vision · 2011.
Abstract. Given a finite set in a metric space, the topological analysis generalizes hierarchical clustering using a 1-parameter.
This formulation is applicable to all Bregman divergences (a broad family of loss functions that includes squared Euclidean distance, KL-divergence), and ...
This is a survey paper in which we explore the connection between graph representations and dissimilarity measures from an information-theoretic perspective ...
Divergence measures provide a means to measure the pairwise dissimilarity between "objects," e.g., vectors and probability density functions (pdfs).
Missing: graphs. | Show results with:graphs.
[PDF] total bregman divergence, a robust divergence measure, and its
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A geometrical illustration of the difference between TBD and BD is given in Figure. 2-1. df (x,y) (the dotted line) is Bregman divergence between x and y based ...
May 13, 2024 · This paper aims to develop a new hierarchical clustering algorithm for multi-graphs, the HTGM algorithm. This algorithm represents the set of graphs in the ...