Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
2005 Special issue: Incremental learning of feature space and classifier for face recognition
Neural Networks - 2005 Special issue: IJCNN 2005
A fast incremental kernel principal component analysis for online feature extraction
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
Algorithms for accelerated convergence of adaptive PCA
IEEE Transactions on Neural Networks
Incremental Learning of Chunk Data for Online Pattern Classification Systems
IEEE Transactions on Neural Networks
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In this paper, we propose a new Chunk IPCA algorithm in which an optimal threshold of accum ulation ratio is adaptively selected such that the classification accuracy is maximized for a validation data set. In order to obtain a proper set of validation data, an online clustering method called Evolving Clustering Method (ECM) is introduced into Chunk IPCA. In the proposed Chunk IPCA called CIPCA-ECM, training data are first separated into the subsets of every class; then, ECM is applied to each subset to update the validation data set. In the experiments, the evaluation of the proposed Chunk IPCA algorithm is carried out using the four VCI data sets and the effectiveness of updating the threshold is discussed. The results suggest that the incremental learning of an eigenspace in the proposed CIPCA-ECM is stably carried out, and a compact and effective eigenspace is obtained over the entire learning stages. The recognition accuracy of CIPCAECM is almost equal to the best performance of CIPCA-FIX in which an optimal threshold is manually predetermined