Self Supervised Boosting

Boosting algorithms and successful applications thereof abound for clas- sification and regression learning problems, but not for unsupervised learning. We propose a sequential approach to adding features to a ran- dom field model by training them to improve classification performance between the data and an equal-sized sample of "negative examples" gen- erated from the model's current estimate of the data density. Next, a fea- ture is trained to improve classification performance between data and negative examples. Finally, a coefficient is learned which determines the importance of this feature relative to ones already in the pool. Negative examples only need to be generated once to learn each new feature.