Handling missing values and censored data in PCA of pharmacological matrices

ABSTRACT

Experimental results often present a substantial fraction of missing and censored values. Here we propose a strategy to perform principal component analysis under this specific incomplete information hypothesis. This allows the reconstruction of the missing information in a way consistent with the experimental observations.

REFERENCES

• 1: I. T. Jolliffe. Principal Component Analysis. Springer, NY, 2002.
• 2: H. Wold. Soft modelling with latent variables: the nonlinear iterative partial least squares approach. In Perspectives in probability and Statistics: Papers in honour of M. S. Barlett, pages 114--142. J. Gani, 1975.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.3 Deduction and Theorem Proving
          Subjects: Uncertainty, "fuzzy," and probabilistic reasoning

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Statistical computing

  I. Computing Methodologies
  I.6 SIMULATION AND MODELING
      I.6.4 Model Validation and Analysis
      I.6.5 Model Development
          Subjects: Modeling methodologies

General Terms:
Algorithms, Design, Performance, Theory