Uncovering Shared Structures in Multiclass Classi�cation
Yonatan Amit - Hebrew University, Jerusalem, Isarel Michael Fink - Hebrew University, Jerusalem, Isarel Nathan Srebro - Toyota Technological Institute at Chicago, U.S. Shimon Ullman - Weizmann Institute of Science, Israel
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.