ISL-GKFDA: An incomplete supervision manifold learning framework based on propagation graph of data

Abstract

To improve manifold classification results using unlabeled data with high dimensional redundancy, we propose an incomplete supervision manifold learning framework. In this framework, Euclidean distance and Euclidean neighborhood are replaced by manifold distance and manifold neighborhood respectively. The pseudo label of unlabeled data is generated by constructing label propagation network in manifold neighborhood. By adding the local manifold neighborhood structure into KFDA (Kernel Fisherś Discriminant Analysis) as regularization term, the local manifold structure is stressed. Subsequently, we propose the ISL-GKFDA (Incomplete Supervision Learning (ISL) with propagation Graph of data in Kernel Fisher Discriminant Analysis) algorithm. We validate ISL-GKFDA by comparing it with FDA (Fisher's Discrimination Analysis), KFDA (Kernel FDA), ISL-FDA (ISL version FDA), ISL-KFDA (ISL version KFDA), SSDA (semi-supervised discriminant analysis based on manifold distance), and KSSDA (Kernel SSDA). In typical manifold datasets, ISL-GKFDA can more accurately capture the boundaries of binary classification; and visually in handwritten digital dataset, ISL-GKFDA can separate three data types more clearly. Finally, we use the UCI database and compare multiple algorithms. When measured by accuracy, the proposed ISL-GKFDA reaches a maximum in at least three datasets of five when labeling ratio is 5%, 15% and 30%, respectively. When measured by F1, ISL-GKFDA reaches the maximum in all datasets.