Fifth IEEE International Conference on Data Mining (ICDM'05) (2005)
Nov. 27, 2005 to Nov. 30, 2005
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2005.76
Yi Huang , University of Munich
Kai Yu , Siemens Corporate Technology
Matthias Schubert , University of Munich
Shipeng Yu , University of Munich
Volker Tresp , Siemens Corporate Technology
Hans-Peter Kriegel , University of Munich
Organizing textual documents into a hierarchical taxonomy is a common practice in knowledge management. Beside textual features, the hierarchical structure of directories reflect additional and important knowledge annotated by experts. It is generally desired to incorporate this information into text mining processes. In this paper, we propose hierarchy-regularized latent semantic indexing, which encodes the hierarchy into a similarity graph of documents and then formulates an optimization problem mapping each document into a low dimensional vector space. The new feature space preserves the intrinsic structure of the original taxonomy and thus provides a meaningful basis for various learning tasks like visualization and classification. Our approach employs the information about class proximity and class specificity, and can naturally cope with multi-labeled documents. Our empirical studies show very encouraging results on two real-world data sets, the new Reuters (RCV1) benchmark and the Swissprot protein database.
K. Yu, Y. Huang, H. Kriegel, S. Yu, M. Schubert and V. Tresp, "Hierarchy-Regularized Latent Semantic Indexing," Fifth IEEE International Conference on Data Mining (ICDM'05)(ICDM), Houston, Texas, 2005, pp. 178-185.