Transductive learning: Learning Iris data with two labeled data

Abstract

This paper presents two graph-based algorithms for solving the transductive learning problem. Stochastic contraction algorithms with similarity based sampling and normalized similarity based sampling are introduced. The transductive learning on a classical problem of plant iris classification achieves an accuracy of 96% with only 2 labeled data while previous research has often used 100 training samples. The quality of the algorithm is also empirically evaluated on a synthetic clustering problem and on the iris plant data.