부산대학교 도서관

BACKGROUND: Environmental health researchers often aim to identify sources or behaviors that give rise to potentially harmful environmental exposures. OBJECTIVE: We adapted principal component pursuit (PCP)—a robust and well-established technique for dimensionality reduction in computer vision and signal processing—to identify patterns in environmental mixtures. PCP decomposes the exposure mixture into a low-rank matrix containing consistent patterns of exposure across pollutants and a sparse matrix isolating unique or extreme exposure events. METHODS: We adapted PCP to accommodate nonnegative data, missing data, and values below a given limit of detection (LOD). We simulated data to represent environmental mixtures of two sizes with increasing proportions