Unified feature extraction framework based on contrastive learning

Feature extraction is an efficient approach for alleviating the issue of dimensionality in high-dimensional data. Contrastive learning (CL), which is a popular self-supervised learning method, has recently attracted considerable attention. In this study, based on a new perspective of CL, we propose a unified framework that is suitable for both unsupervised and supervised feature extraction. In the framework, two CL graphs are first constructed to define the positive and negative pairs uniquely. Subsequently, the projection matrix is determined by minimizing the contrastive loss function. Moreover, the proposed framework considers positive and negative pairs to unify the unsupervised and supervised feature extraction. We propose three specific methods under this framework: unsupervised CL, supervised CL without local preservation, and supervised CL with local preservation. Finally, numerical experiments on six real datasets demonstrate the superior performance of the proposed framework compared to existing methods.