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Principal component analysis (PCA) is a commonly used multivariate procedure that reduces the dimensionality of a data set. When applied to quantitative electroencephalogram (qEEG) data, PCA produces components that may represent functional systems within the brain. Unfortunately, qEEG, like many other physiological measures, produce distributions that are positively skewed. In response, researchers...
To Solve the problem of low accuracy and high false alarm, a construction method of Bagging ensemble based on random subspace PCA (Principle Component Analysis) was proposed. To create a training data for a base classifier, the feature set is randomly split into several subsets and PCA is applied to each subset. all principal components are retained to keep the variety information in the data; To...
An electronic nose (PEN 2) comprising ten metal oxide semiconductor sensors (W1C, W5S, W3C, W6S, W5C, W1S, W1W, W2S, W2W, W3S) were used to detection of five different stored duration wheats (wheats were harvested from 2000 to 2004, and named as W00, W01, W02, W03, and W04, respectively). A few of sensors were switched-off by multivariate analysis of variance and loading analysis. The responses signals...
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