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An iterative approach to local-PCA

John, S. N. and Wersing, Heiko and Ritter, Helge (2010) An iterative approach to local-PCA. In: Neural Networks (IJCNN), The 2010 International Joint Conference on, 18-23 July 2010, Barcelona, Spain.

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We introduce a greedy algorithm that works from coarse to fine by iteratively applying localized principal component analysis (PCA). The decision where and when to split or add new components is based on two antagonistic criteria. Firstly, the well known quadratic reconstruction error and secondly a measure for the homogeneity of the distribution. For the latter criterion, which we call “generation error”, we compared two different possible methods to assess if the data samples are distributed homogeneously. The proposed algorithm does not involve a costly multi-objective optimization to find a partition of the inputs. Further, the final number of local PCA units, as well as their individual dimensionality need not to be predefined. We demonstrate that the method can flexibly react to different intrinsic dimensionalities of the data.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Principal component analysis, Manifolds, Histograms, Image reconstruction, Equations, Measurement uncertainty, Partitioning algorithms
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Mrs Patricia Nwokealisi
Date Deposited: 01 Feb 2018 13:50
Last Modified: 01 Feb 2018 13:50

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