PI: Dapeng Wu

Principal Component Analysis (PCA) has been widely used in computer vision and machine learning applications due to its excellent performance in compression, feature extraction and feature representation. However, PCA suffers from severely degraded performance when outliers exist in datasets.  To address this issue, the project is intended to develop a robust PCA algorithm, capable of mitigating outliers.  The key idea is to leverage a popularity index for each sample so that outliers will contribute little in finding the projection matrix of the PCA.