Semi-supervised Learning Based on Label Propagation through Submanifold

A semi-supervised learning algorithm is proposed based Oil label propagation through submanifold. The algorithm assumes that. samples lying in a local neighborhood share the same labels and the global labels changing among submanifolds is sufficiently smooth. The algorithm firstly introduces a k-nearest neighbor graph to describe local neighborhood among the data, set. And then, a cost function and a constraint equation are proposed, which stand for the global smoothness of the class labels' changing and the labeled samples' information respectively. The final semi-supervised learning task is converted to a typical quadratic program, whose optimal solution call minimize the cost function and satisfy the supervised constraint. Experimental results of the algorithm on toy data, digit recognition, and text classification demonstrate the feasibility and efficiency of the proposed algorithm.