Self-Paced Two-dimensional PCA
Keywords:Dimensionality Reduction/Feature Selection, Unsupervised & Self-Supervised Learning, Optimization, Learning Theory
AbstractTwo-dimensional PCA (2DPCA) is an effective approach to reduce dimension and extract features in the image domain. Most recently developed techniques use different error measures to improve their robustness to outliers. When certain data points are overly contaminated, the existing methods are frequently incapable of filtering out and eliminating the excessively polluted ones. Moreover, natural systems have smooth dynamics, an opportunity is lost if an unsupervised objective function remains static. Unlike previous studies, we explicitly differentiate the samples to alleviate the impact of outliers and propose a novel method called Self-Paced 2DPCA (SP2DPCA)algorithm, which progresses from `easy’ to `complex’ samples. By using an alternative optimization strategy, SP2DPCA looks for optimal projection matrix and filters out outliers iteratively. Theoretical analysis demonstrates the robustness nature of our method. Extensive experiments on image reconstruction and clustering verify the superiority of our approach.
How to Cite
Li, J., Kang, Z., Peng, C., & Chen, W. (2021). Self-Paced Two-dimensional PCA. Proceedings of the AAAI Conference on Artificial Intelligence, 35(9), 8392-8400. https://doi.org/10.1609/aaai.v35i9.17020
AAAI Technical Track on Machine Learning II