A Unified Framework for Feature Extraction based on Contrastive Learning

Feature extraction is an efficient approach for alleviating the curse of dimensionality in high-dimensional data. With the development of contrastive learning in the field of self-supervised learning, we propose a unified framework for feature extraction based on contrastive learning from a new perspective, which is suitable for both unsupervised and supervised feature extraction. In this framework, we first construct a contrastive learning graph based on graph embedding (GE), which proposes a new way to define positive and negative pairs. Then, we solve the projection matrix by minimizing the contrastive loss function. In this framework, we can consider not only similar samples but also dissimilar samples on the basis of unsupervised GE, so as to narrow the gap with supervised feature extraction. In order to verify the effectiveness of our proposed framework for unsupervised and supervised feature extraction, we improved the unsupervised GE method LPP with local preserving, the supervised GE method LDA without local preserving, and the supervised GE method LFDA with local preserving, and proposed CL-LPP, CL-LDA, and CL-LFDA, respectively. Finally, we performed numerical experiments on five real datasets