A data-dependent distance measure for transductive instance-based learning

— We consider learning in a transductive setting using instance-based learning (k-NN) and present a method for constructing a data-dependent distance “metric” using both labeled training data as well as available unlabeled data (that is to be classified by the model). This new data-driven measure of distance is empirically studied in the context of various instance-based models and is shown to reduce error (compared to traditional models) under certain learning conditions. Generalizations and improvements are suggested.

• Type: Conference
• Year: 2007
• Where: SMC
• Authors: Jared Lundell, Dan Ventura
• Keywords: Available Unlabeled Data, Control Systems, Data-dependent Distance, SMC 2007, Transductive Setting