In this study we propose sketching algorithms for computing similarities between hierarchical data. Specifically, we look at data objects that are represented using leaf-labeled t...
Similarity search and data mining often rely on distance or similarity functions in order to provide meaningful results and semantically meaningful patterns. However, standard dist...
Tobias Emrich, Franz Graf, Hans-Peter Kriegel, Mat...
In this paper, we introduce a new approach for content-based similarity search for brain images. Based on the keyblock representation, our framework employs the Principal Componen...
Co-citation (number of nodes linking to both of a given pair of nodes) is often used heuristically to judge similarity between nodes in a complex network. We investigate the relat...
We study metric learning as a problem of information retrieval. We present a general metric learning algorithm, based on the structural SVM framework, to learn a metric such that ...