In this paper, we introduce a generic framework for semi-supervised kernel learning. Given pairwise (dis-)similarity constraints, we learn a kernel matrix over the data that respe...
Sparse kernel regressors have become popular by applying the support vector method to regression problems. Although this approach has been shown to exhibit excellent generalization...
The main goal of this paper is to prove inequalities on the reconstruction error for Kernel Principal Component Analysis. With respect to previous work on this topic, our contribu...
The quality of large-scale recommendation systems has been insufficient in terms of the accuracy of prediction. One of the major reasons is caused by the sparsity of the samples, ...
Relations between entities in text have been widely researched in the natural language processing and informationextraction communities. The region connecting a pair of entities (...