4 citations · 5 across the 5 of their papers we have counts for
3 papers · 1 filter
Robust functional PCA for relative data
Jeremy Oguamalam, Peter Filzmoser, Karel Hron +2
This paper introduces a robust approach to functional principal component analysis (FPCA) for relative data, particularly density functions. While recent papers have studied densit…
Sparse outlier-robust PCA for multi-source data
Patricia Puchhammer, Ines Wilms, Peter Filzmoser
Sparse and outlier-robust Principal Component Analysis (PCA) has been a very active field of research recently. Yet, most existing methods apply PCA to a single dataset whereas mul…
Robust covariance estimation and explainable outlier detection for matrix-valued data
Marcus Mayrhofer, Una Radojičić, Peter Filzmoser
This work introduces the Matrix Minimum Covariance Determinant (MMCD) method, a novel robust location and covariance estimation procedure designed for data that are naturally repre…