Shouwei Sun

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The classical maximum entropy clustering (MEC) algorithm usually cannot achieve satisfactory results in the situations where the data is insufficient, incomplete, or distorted. To address this problem, inspired by transfer learning, the specific cluster prototypes and fuzzy memberships jointly leveraged (CPM-JL) framework for cross-domain MEC (CDMEC) is(More)
Conventional, soft-partition clustering approaches, such as fuzzy c-means (FCM), maximum entropy clustering (MEC) and fuzzy clustering by quadratic regularization (FC-QR), are usually incompetent in those situations where the data are quite insufficient or much polluted by underlying noise or outliers. In order to address this challenge, the quadratic(More)
The classical maximum entropy clustering (MEC) algorithm can only work on a single dataset, which might result in poor effectiveness in the condition that the capacity of the dataset is insufficient. To resolve this problem, using the strategy of transfer learning, this paper proposed the novel transfer learning based maximum entropy clustering (TL_MEC)(More)
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