Semi-supervised learning by locally linear embedding in kernel space

Graph based semi-supervised learning methods (SSL) implicitly assume that the intrinsic geometry of the data points can be fully specified by an Euclidean distance based local neighborhood graph, however, this assumption may not always be necessarily true. To overcome this problem, we propose to apply locally linear embedding (LLE) method to characterize the geometric structure of the data points; besides this, the embedding process is performed in the kernel induced feature space rather than the original input space. After embedding, the proposed transductive learning method predicts the labels of the unlabeled data within the regularization framework. Experimental results on image retrieval and pattern recognition verify the performance of the proposed approach.