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Robust PCA with Intra-Sample Outlier Process Based on Fuzzy Mahalanobis Distances and Noise Clustering

To make principal component analysis (PCA) robust for intra-sample noise, Torre and Black proposed a general analogue outlier process that provides a connection to robust M-estimation. This paper proposes a fuzzy membership approach based on the squared Mahalanobis distances and the noise clustering (NC) by Dave for robustizing PCA to intra-sample outliers