Self Supervised Boosting
Welling, Max, Zemel, Richard S., Hinton, Geoffrey E.
–Neural Information Processing Systems
Boosting algorithms and successful applications thereof abound for classification andregression learning problems, but not for unsupervised learning. We propose a sequential approach to adding features to a random fieldmodel by training them to improve classification performance between the data and an equal-sized sample of "negative examples" generated fromthe model's current estimate of the data density.
Neural Information Processing Systems
Dec-31-2003