Preprocessing | Exploring the raw data | Data standardization | Principal component pursuit | Model selection | Grid search for parameter tuning | Running PCP | The recovered L matrix | The recovered S matrix | PCP and NMF for source apportionment | PCP-NMF Loadings | Variance Explained | Loadings replotted | PCP-NMF Scores | Conclusion | References

Simulating data | Model selection | Grid search for parameter tuning | Running PCP | Benchmarking with PCA | Evaluating PCP against the ground truth | After PCP

PCP modeling overview | The low-rank matrix | The sparse matrix | Extensions for environmental health data | Missing value functionality | Leveraging potential limit of detection (LOD) information | Non-negativity constraint on the estimated L matrix | Convex vs. non-convex PCP | Convex PCP | Non-convex PCP | PCP parameters | Intuition behind lambda, mu, and eta | Theoretically optimal parameters | Empirically optimal parameters | Tuning parameters with grid_search_cv() | Cross-validation procedure | Best practices for $\xi$ and $K$ | Interpretation of results | Coded example analyses | References