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This paper presents a novel unsupervised method for mining time series based on two generative topic models, i.e., probabilistic Latent Semantic Analysis (pLSA) and Latent Dirichlet Allocation (LDA). The proposed method treats each time series as a text document, and extracts a set of local patterns from the sequence as words by sliding a short temporal window along the sequence. Motivated by the...
Latent topic models such as Latent Dirichlet Allocation (LDA) and probabilistic Latent Semantic Analysis (pLSA) have demonstrated success in computer vision tasks. Most existing approaches train LDA and pLSA in an unsupervised manner, where the training data does not include any class label information. However, the class labels in training data are very important for the task of classification. In...
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