Efficient co-regularised least squares regression

In many applications, unlabelled examples are inexpensive and easy to obtain. Semisupervised approaches try to utilise such examples to reduce the predictive error. In this paper, we investigate a semi-supervised least squares regression algorithm based on the co-learning approach. Similar to other semisupervised algorithms, our base algorithm has cubic runtime complexity in the number of unlabelled examples. To be able to handle larger sets of unlabelled examples, we devise a semi-parametric variant that scales linearly in the number of unlabelled examples. Experiments show a significant error reduction by co-regularisation and a large runtime improvement for the semi-parametric approximation. Last but not least, we propose a distributed procedure that can be applied without collecting all data at a single site.

• Type: Conference
• Year: 2006
• Where: ICML
• Authors: Stefan Wrobel, Thomas Gärtner, Tobias Scheffer, Ulf Brefeld
• Keywords: Cubic Runtime Complexity, ICML 2006, Machine Learning, Squares Regression Algorithm, Unlabelled Examples