Robust semi-supervised discriminant embedding method with soft label in kernel space

Pei Peng, Yong-Ping Zhao

Abstract: Considering some problems of local linear embedding methods in semi-supervised scenarios, a robust scheme for generating soft labels is designed and a semi-supervised discrimination embedding method combined with soft labels in the kernel space is proposed in this paper. The method uses the kernel trick to perform the following related operations in the regenerative kernel Hilbert space. Firstly, in order to deal with the different distribution of labeled data, the confidence of generating soft labels is introduced in the label propagation process, and then the label density of data within the hypersphere whose unlabeled data is the center of the sphere is used to generate final soft labels. The scheme is experimentally demonstrated to generate more reliable soft labels even with lower labeling ratios. In order to make full use of the prior information provided by soft labels and the confidence of soft labels, a distance distortion function is used in feature learning to introduce the soft label prior information, and a penalty term about the global information is added to the optimization objective function, so that a more suitable low-dimensional representation for data classification can be obtained. Finally, the method is experimentally compared with various feature learning methods and visualized on handwritten digit dataset. The experiments show that this method performs well on various datasets of the UCI database and is successfully applied in image recognition, especially when the ratio of labeled data is in a low range.