Almost autonomous training of mixtures of principal component analyzers

2004-07-02
Musa, MEM
de Ridder, D
Duin, RPW
Atalay, Mehmet Volkan
In recent years, a number of mixtures of local PCA models have been proposed. Most of these models require the user to set the number of submodels (local models) in the mixture and the dimensionality of the submodels (i.e., number of PC's) as well. To make the model free of these parameters, we propose a greedy expectation-maximization algorithm to find a suboptimal number of submodels. For a given retained variance ratio, the proposed algorithm estimates for each submodel the dimensionality that retains this given variability ratio. We test the proposed method on two different classification problems: handwritten digit recognition and 2-class ionosphere data classification. The results show that the proposed method has a good performance.

Citation Formats
M. Musa, D. de Ridder, R. Duin, and M. V. Atalay, “Almost autonomous training of mixtures of principal component analyzers,” PATTERN RECOGNITION LETTERS, vol. 25, no. 9, pp. 1085–1095, 2004, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39276.