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Data analysis including outlier is more difficult to the analysis without outlier. The outlier has a chance to increase the misclassification rate and the variance of estimate in the supervised learning like classification and regression. Also the outlier becomes a cluster in the clustering as unsupervised learning. So we are hard to represent the clustering result. Because of the previous problems,...
Principal components analysis (PCA) is an important approach to unsupervised dimensionality reduction. However, principal components (PCs) are a set of new variables carrying no clear physical meanings and still require all the original variables. To deal with this problem, the PC dominant feature (PCDF) is defined. Then, feature selection using them is considered and a new algorithm for determining...
It is important to reduce the dimensionality of features in Web Chinese text categorization. Isomap algorithm is an unsupervised manifold learning technique. SIIsomap algorithm, an extension of Isomap to supervised feature extraction, is proposed in this paper. It uses adding constant method and a direct embedding technique of Isomap algorithm for testing data to make the embedding more reasonable...
Event-related potentials (ERP) are brain electrophysiological patterns created by averaging electroencephalographic (EEG) data, time-locking to events of interest (e.g., stimulus or response onset). In this paper, we propose a semi-automatic framework for mining ERP data, which includes the following steps: PCA decomposition, extraction of summary metrics, unsupervised learning (clustering) of patterns,...
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