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When classifiers are deployed in real-world applications, it is assumed that the distribution of the incoming data matches the distribution of the data used to train the classifier. This assumption is often incorrect, which necessitates some form of change detection or adaptive classification. While there has been a lot of work on change detection based on(More)
While there is a lot of research on change detection based on the streaming classification error, finding changes in multidimensional unlabelled streaming data is still a challenge. Here we propose to apply principal component analysis (PCA) to the training data, and mine the stream of selected principal components for change in the distribution. A recently(More)
A change detection algorithm for multi-dimensional data reduces the input space to a single statistic and compares it with a threshold to signal change. This study investigates the performance of two methods for estimating such a threshold: bootstrapping and control charts. The methods are tested on a challenging dataset of emotional facial expressions,(More)
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