  Principal Component Analysis (PCA) is a popular tool for dimensionality
reduction and feature extraction in data analysis. There is a probabilistic
version of PCA, known as Probabilistic PCA (PPCA). However, standard PCA and
PPCA are not robust, as they are sensitive to outliers. To alleviate this
problem, this paper introduces the Self-Paced Learning mechanism into PPCA, and
proposes a novel method called Self-Paced Probabilistic Principal Component
Analysis (SP-PPCA). Furthermore, we design the corresponding optimization
algorithm based on the alternative search strategy and the
expectation-maximization algorithm. SP-PPCA looks for optimal projection
vectors and filters out outliers iteratively. Experiments on both synthetic
problems and real-world datasets clearly demonstrate that SP-PPCA is able to
reduce or eliminate the impact of outliers.
