  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.
