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Time-evolving data sets can often be arranged as a higher-order tensor with
one of the modes being the time mode. While tensor factorizations have been
successfully used to capture the underlying patterns in such higher-order data
sets, the temporal aspect is often ignored, allowing for the reordering of time
points. In recent studies, temporal regularizers are incorporated in the time
mode to tackle this issue. Nevertheless, existing approaches still do not allow
underlying patterns to change in time (e.g., spatial changes in the brain,
contextual changes in topics). In this paper, we propose temporal PARAFAC2
(tPARAFAC2): a PARAFAC2-based tensor factorization method with temporal
regularization to extract gradually evolving patterns from temporal data.
Through extensive experiments on synthetic data, we demonstrate that tPARAFAC2
can capture the underlying evolving patterns accurately performing better than
PARAFAC2 and coupled matrix factorization with temporal smoothness
regularization.
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