Multi-layer graph-based semi-supervised learning for large-scale image datasets using mapreduce

Wen-Yu Lee, Liang-Chi Hsieh, Guan-Long Wu, Winston H. Hsu, Ya-Fan Su

Abstract: Semi-supervised learning is to exploit the vast amount of unlabeled data in the world. This paper proposes a scalable graph-based technique leveraging the distributed computing power of the MapReduce programming model. For a higher quality of learning, the paper also presents a multi-layer learning structure to unify both visual and textual information of image data during the learning process. Experimental results show the effectiveness of the proposed methods.