Kernel factor analysis with Varimax rotation

Darryl Charles, Colin Fyfe

Research output: Contribution to conferencePaperpeer-review

6 Citations (Scopus)


Kernel methods have recently become popular for the exploration of structure in data and one of the more commonly used methods is Kernel Principal Components Analysis (Kernel PCA). This method is similar to non-linear PCA in that PCA is performed in kernel space, which is a non-linear transformation of the data into a higher dimension. We compare this method to a closely related statistic technique called Factor Analysis and show that, particularly when used in conjunction with a Varimax rotation of the factor axis, we can transform the kernel space so that the local variance in data clusters may be accounted for and not just the global variance across all of the data clusters. When the data is matched with an appropriate kernel then this method improves the interpretability of the results.

Original languageEnglish
Number of pages6
Publication statusPublished (in print/issue) - 2000
EventInternational Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy
Duration: 24 Jul 200027 Jul 2000


ConferenceInternational Joint Conference on Neural Networks (IJCNN'2000)
CityComo, Italy


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