Principal Component Pursuit for Pattern Identification in Environmental Mixtures

Environmental health researchers often aim to identify sources/behaviors that give rise to potentially harmful exposures. We adapted principal component pursuit (PCP)-a robust 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 exposure patterns across pollutants and a sparse matrix isolating unique exposure events. We adapted PCP to accommodate non-negative and missing data, and values below a given limit of detection (LOD). We simulated data to represent environmental mixtures of two sizes with increasing proportions

Keywords:

• Sparse matrix
• Matrix (chemical analysis)
• Principal component analysis
• Missing data
• Pattern recognition
• principal component pursuit
• Singular value decomposition
• Mathematics
• Dimensionality reduction
• Noise
• Artificial intelligence

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