title: Equivalence of some common linear feature extraction techniques for appearance-based object recognition tasks.
abstract: Recently, a number of empirical studies have compared the performance of PCA and ICA as feature extraction methods in appearance-based object recognition systems, with mixed and seemingly contradictory results. In this paper, we briefly describe the connection between the two methods and argue that whitened PCA may yield identical results to ICA in some cases. Furthermore, we describe the specific situations in which ICA might significantly improve on PCA.
authors.affiliation: Department of Industrial Systems Engineering, Miguel Hernandez University, Alicante, Spain. suni@umh.es
authors.forename: M
authors.initials: M
authors.lastname: Asunción Vicente
authors.forename: Patrik O
authors.initials: PO
authors.lastname: Hoyer
authors.forename: Aapo
authors.initials: A
authors.lastname: Hyvärinen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016014:Linear Models; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity