In Proceedings of the 2009 SIAM International Conference on Data Mining, pages: 1099-1110, (Editors: Park, H. , S. Parthasarathy, H. Liu), Philadelphia, PA, USA, Society for Industrial and Applied Mathematics, SDM, May 2009 (inproceedings)
We propose Link Propagation as a new semi-supervised learning
method for link prediction problems, where the task is to predict
unknown parts of the network structure by using auxiliary information
such as node similarities. Since the proposed method can
fill in missing parts of tensors, it is applicable to multi-relational
domains, allowing us to handle multiple types of links simultaneously.
We also give a novel efficient algorithm for Link Propagation
based on an accelerated conjugate gradient method.
In Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia Conference, pages: 1030-1037, (Editors: Theeramunkong, T. , B. Kijsirikul, N. Cercone, T. B. Ho), Springer, Berlin, Germany, PAKDD, April 2009 (inproceedings)
Pairwise classification has many applications including network prediction, entity resolution, and collaborative filtering. The pairwise kernel has been proposed for those purposes by several research groups independently, and become successful in various fields. In this paper, we propose an efficient alternative which we call Cartesian kernel. While the existing pairwise kernel (which we refer to as Kronecker kernel) can be interpreted as the weighted adjacency matrix of the Kronecker product graph of two graphs, the Cartesian kernel can be interpreted as that of the Cartesian graph which is more sparse than the Kronecker product graph. Experimental results show the Cartesian kernel is much faster than the existing pairwise kernel, and at the same time, competitive with the existing pairwise kernel in predictive performance.We discuss the generalization bounds by the two pairwise kernels by using eigenvalue analysis of the kernel matrices.
Kashima, H., Yamazaki, K., Saigo, H., Inokuchi, A.
Regression with Intervals
International Workshop on Data-Mining and Statistical Science (DMSS2007), October 2007, JSAI Incentive Award. Talk was given by Hisashi Kashima. (talk)
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems