Deep Semi-Supervised Learning via Tensor Label Propagation for High-Dimension-Low-Sample-Size Data.

Abstract

Semi-supervised learning (SSL) aims to effectively utilize a small amount of labeled data together with a large volume of unlabeled data to improve learning performance. Among various SSL strategies, label propagation has been widely adopted due to its ability to diffuse label information across data points via graph structures. However, most existing label propagation-based SSL methods struggle with high-dimension-low-sample-size (HDLSS) data, as they rely on pairwise similarity measures that fail to capture the complex relationships among samples in such settings. To overcome this limitation, we propose a novel deep SSL framework that enhances label propagation using tensor-based similarity, enabling the modeling of high-order relationships among multiple samples. Specifically, we first pretrain a feature extraction network using the labeled data to obtain initial feature representations. Subsequently, tensor label propagation and fine-tuning of the feature extraction network are conducted iteratively. In the tensor label propagation module, pseudo-labels for the unlabeled samples are estimated more accurately by leveraging high-order similarity. These pseudo-labels, along with the original labeled data, are then used to fine-tune the pretrained network, resulting in more robust and discriminative feature representations across all samples. By embedding high-order structural information into the SSL pipeline, our method significantly enhances the prediction performance from limited labeled data in the HDLSS setting. Extensive experiments on multiple HDLSS datasets demonstrate the superiority of our approach compared to recent baselines.