Abstract
Fuzzy c-Varieties (FCV) is the linear fuzzy clustering algorithm that estimates local principal component vectors as the vectors spanning prototypes of clusters. However, least squares techniques often fail to account for outliers, which are common in real applications. In this paper, we propose a technique for making the FCV algorithm robust to intra-sample outliers. The objective function based on the lower rank approximation of the data matrix is minimized by a robust M-estimation algorithm that is similar to FCM-type iterative procedures.
