  In this study, we propose a feature extraction framework based on contrastive
learning with adaptive positive and negative samples (CL-FEFA) that is suitable
for unsupervised, supervised, and semi-supervised single-view feature
extraction. CL-FEFA constructs adaptively the positive and negative samples
from the results of feature extraction, which makes it more appropriate and
accurate. Thereafter, the discriminative features are re extracted to according
to InfoNCE loss based on previous positive and negative samples, which will
make the intra-class samples more compact and the inter-class samples more
dispersed. At the same time, using the potential structure information of
subspace samples to dynamically construct positive and negative samples can
make our framework more robust to noisy data. Furthermore, CL-FEFA considers
the mutual information between positive samples, that is, similar samples in
potential structures, which provides theoretical support for its advantages in
feature extraction. The final numerical experiments prove that the proposed
framework has a strong advantage over the traditional feature extraction
methods and contrastive learning methods.
