Enhanced PSSV for Incomplete Data Analysis

Partial Sum Minimization of Singular Values (PSSV) is a powerful tool for image denoising, matrix completion and recovering underlying low-rank structure from the corrupted data via Partial Sum Minimization of Singular Values. However, the performance of PSSV degenerates remarkably when data is incomplete or some data is corrupted completely, which is usually faced in real applications especial for video sequence analysis. To handle this problem, we impose the variance regularization in PSSV by analyzing PSSV and truncated nuclear norm. Our proposed model benefits from 1) the robustness of principal components to outliers and missing values. 2) It can guarantee the compactness of the learned clean data. 3) It can recover the missing sample data from the data matrix. Experimental results on background extraction from the incomplete videos and data, image denoising, and clustering illustrate the effectiveness of the proposed approach.