Hierarchical mixtures of experts and the EM algorithm
Neural Computation
A subclass model for non-linear pattern classification
Pattern Recognition Letters
Mixtures of probabilistic principal component analyzers
Neural Computation
Unsupervised classification with non-Gaussian mixture models using ICA
Proceedings of the 1998 conference on Advances in neural information processing systems II
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the manifolds of images of handwritten digits
IEEE Transactions on Neural Networks
Deformation Models for Image Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploration of heterogeneous FPGAs for mapping linear projection designs
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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This paper proposes a method for recognizing the numeral characters based on the PCA (Principal Component Analysis) mixture model. The proposed method is motivated by the idea that the classification accuracy is improved by modeling each class into a mixture of several components and by performing the classification in the compact and decorrelated feature space. For realizing the idea, each numeral class is partitioned into several clusters and each cluster's density is estimated by a Gaussian distribution function in the PCA transformed space. The parameter estimation is performed by an iterative EM (Expectation Maximization) algorithm, and model order is selected by a fast sub-optimal validation scheme. The proposed method is also computation-effective because the optimal feature components for a cluster are determined by a sequential elimination of insignificant feature due to the ordering property of the significance among the feature components in the PCA transformed space. Simulation results shows that the proposed recognition method outperforms other methods such as the k-NN (Nearest Neighbor) method, a single PCA model, or the ICA (Independent Component Analysis) mixture model in terms of recognition accuracy.