Graph based multi-class semi-supervised learning using Gaussian process

Abstract

This paper proposes a multi-class semi-supervised learning algorithm of the graph based method. We make use of the Bayesian framework of Gaussian process to solve this problem. We propose the prior based on the normalized graph Laplacian, and introduce a new likelihood based on softmax function model. Both the transductive and inductive problems are regarded as MAP (Maximum A Posterior) problems. Experimental results show that our method is competitive with the existing semi-supervised transductive and inductive methods. © Springer-Verlag Berlin Heidelberg 2006.