Discriminative prototype selection methods for graph embedding

作者：Borzeshi Ehsan Zare^*; Piccardi Massimo; Riesen Kaspar; Bunke Horst

来源：Pattern Recognition, 2013, 46(6): 1648-1657.

DOI：10.1016/j.patcog.2012.11.020

• Graph embedding
• Discriminative prototype selection
• Graph classification
• Dissimilarity representation

Graphs possess a strong representational power for many types of patterns. However, a main limitation in their use for pattern analysis derives from their difficult mathematical treatment. One way of circumventing this problem is that of transforming the graphs into a vector space by means of graph embedding. Such an embedding can be conveniently obtained by using a set of %26quot;prototype%26quot; graphs and a dissimilarity measure. However, when we apply this approach to a set of class-labelled graphs, it is challenging to select prototypes capturing both the salient structure within each class and inter-class separation. In this paper, we introduce a novel framework for selecting a set of prototypes from a labelled graph set taking their discriminative power into account. Experimental results showed that such a discriminative prototype selection framework can achieve superior results in classification compared to other well-established prototype selection approaches.

• 出版日期2013-6