Reconstruction Weighting Principal Component Analysis with Fusion Contrastive Learning

Reconstruction Weighting Principal Component Analysis with Fusion Contrastive Learning

Qianqian Wang, Meiling Liu, Wei Feng, Mengping Jiang, Haiming Xu, Quanxue Gao

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 5091-5098. https://doi.org/10.24963/ijcai.2024/563

Principal component analysis (PCA) is a popular unsupervised dimensionality reduction method to extract the principal components of data. However, there are two problems with the existing PCA: (1) Traditional PCA methods treat each sample equally and ignore sample differences. (2) They fail to extract the discriminative features required by recognition tasks. To overcome these problems, we incorporate contrastive learning to develop a novel weighted PCA algorithm. Specifically, our method weights the reconstruction error of individual samples to reduce the influence of outliers. Besides, it integrates contrastive learning into PCA to increase inter-class distances and reduce intra-class distance, which helps to improve PCA's discriminative capability. We further develop an unsupervised strategy to select positive and negative samples, which eliminates pseudo-negative samples guided by clustering labels. Specifically, it employs confidence level to distinguish positive and negative samples. Consequently, our method achieves higher recognition accuracy on benchmark datasets.
Keywords:
Machine Learning: ML: Feature extraction, selection and dimensionality reduction
Computer Vision: CV: Recognition (object detection, categorization)
Computer Vision: CV: Representation learning
Machine Learning: ML: Classification