Transductive Experiment Design
This paper considers the problem of selecting the most informative experiments x to get measures y for learning an inference model y = f(x). We propose a novel concept for active learning, transductive experiment design, to overcome the shortcomings of existing experiment design methods, e.g. insucient exploration of available unmeasured data and poor scalability for large data sets. In-depth analysis clearly shows that the method tends to favor experiments that are hard to predict and meanwhile typical in representing remaining hard-to-predict data. Ecient solutions are further developed through mathematical programming techniques. Encouraging results on toy problems and real-world data sets are included to highlight the advantages of the proposed approaches.