A new information criterion for the selection of subspace models

The problem of model selection is considerably important for acquiring higher levels of generalization capability in supervised learning. In this paper, we propose a new criterion for model selection named the subspace information criterion (SIC). Computer simulations show that SIC works well even when the number of training examples is small.

• Type: Conference
• Year: 2000
• Where: ESANN
• Authors: Masashi Sugiyama, Hidemitsu Ogawa
• Keywords: ESANN 2000, ESANN 2007, Generalization Capability, Model Selection, Subspace Information Criterion