Uncovering shared structures in multiclass classification

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.

• Type: Conference
• Year: 2007
• Where: ICML
• Authors: Yonatan Amit, Michael Fink 0002, Nathan Srebro, Shimon Ullman
• Keywords: Convex Optimization Problem, ICML 2007, Machine Learning, Multiclass Learning