Semi-supervised Learning via Gaussian Processes

We present a probabilistic approach to learning a Gaussian Process classifier in the presence of unlabeled data. Our approach involves a "null category noise model" (NCNM) inspired by ordered categorical noise models. The noise model reflects an assumption that the data density is lower between the class-conditional densities. We illustrate our approach on a toy problem and present comparative results for the semi-supervised classification of handwritten digits.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Neil D. Lawrence, Michael I. Jordan
• Keywords: NIPS 2004, NIPS 2007, Noise Model, Null Category Noise, Ordered Categorical Noise