Uncovering shared structures in multiclass classification

Yonatan Amit, Michael Fink, Nathan Srebro, Shimon Ullman

Abstract: This paper suggests a method for multiclass learning with many classes by simultaneously learning shared characteristics common to the classes, and predictors for the classes in terms of these characteristics. We cast this as a convex optimization problem, using trace-norm regularization and study gradient-based optimization both for the linear case and the kernelized setting.