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- W1983798833 abstract "This paper proposes a novel temporal knowledge representation and learning framework to perform large-scale temporal signature mining of longitudinal heterogeneous event data. The framework enables the representation, extraction, and mining of high-order latent event structure and relationships within single and multiple event sequences. The proposed knowledge representation maps the heterogeneous event sequences to a geometric image by encoding events as a structured spatial-temporal shape process. We present a doubly constrained convolutional sparse coding framework that learns interpretable and shift-invariant latent temporal event signatures. We show how to cope with the sparsity in the data as well as in the latent factor model by inducing a double sparsity constraint on the β-divergence to learn an overcomplete sparse latent factor model. A novel stochastic optimization scheme performs large-scale incremental learning of group-specific temporal event signatures. We validate the framework on synthetic data and on an electronic health record dataset." @default.
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- W1983798833 date "2013-02-01" @default.
- W1983798833 modified "2023-10-17" @default.
- W1983798833 title "A Framework for Mining Signatures from Event Sequences and Its Applications in Healthcare Data" @default.
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- W1983798833 doi "https://doi.org/10.1109/tpami.2012.111" @default.
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