Many diagrams contain compound objects composed of parts. We propose a recognition framework that learns parts in an unsupervised way, and requires training labels only for compou...
In this paper, we propose a novel stochastic framework for unsupervised manifold learning. The latent variables are introduced, and the latent processes are assumed to characteriz...
Gang Wang, Weifeng Su, Xiangye Xiao, Frederick H. ...
We consider the problem of labeling a partially labeled graph. This setting may arise in a number of situations from survey sampling to information retrieval to pattern recognition...
This paper proposes extending semi-supervised learning by allowing an ongoing interaction between a user and the system. The extension is intended to not only to speed up search fo...
Multi-document discourse analysis has emerged with the potential of improving various NLP applications. Based on the newly proposed Cross-document Structure Theory (CST), this pap...