Multiple factor analysis: principal component analysis for multitable and multiblock data sets

By Hervé Abdi, Lynne J. Williams, Dominique Valentin. 2013

483 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Food Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d10dd1de036400ed2d660c76ad9dae34290bca52

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Food Science

1. Olivier Périn
2. Marie-Pier Scott-Boyer
3. Milan Picard
4. Rob Knight
5. Arnaud Droit
6. Antoine Bodein

In Statistics and Probability

1. Rob Knight

Citations in the corpus

• Integration strategies of multi-omics data for machine learning analysis (2021, background): “Additionally, the size difference between omics datasets can promote a learning imbalance as the algorithm spent more time learning on the omics with the biggest number of variables, overlooking the other omics [66,67].”
• A Novel Sparse Compositional Technique Reveals Microbial Perturbations (2019, methodology): “This centering procedure is commonly used prior to performing PCA and eliminates the need to explicitly compute bias constants (43).”