Robust PCA as Bilinear Decomposition With Outlier-Sparsity Regularization
Robust PCA as Bilinear Decomposition With Outlier-Sparsity Regularization
Principal component analysis (PCA) is widely used for dimensionality reduction, with well-documented merits in various applications involving high-dimensional data, including computer vision, preference measurement, and bioinformatics. In this context, the fresh look advocated here permeates benefits from variable selection and compressive sampling, to robustify PCA against outliers. A least-trimmed squares …