Kavli Affiliate: Feng Wang
| First 5 Authors: Lianmeng Jiao, Feng Wang, Zhun-ga Liu, Quan Pan,
| Summary:
As a representative evidential clustering algorithm, evidential c-means (ECM)
provides a deeper insight into the data by allowing an object to belong not
only to a single class, but also to any subset of a collection of classes,
which generalizes the hard, fuzzy, possibilistic, and rough partitions.
However, compared with other partition-based algorithms, ECM must estimate
numerous additional parameters, and thus insufficient or contaminated data will
have a greater influence on its clustering performance. To solve this problem,
in this study, a transfer learning-based ECM (TECM) algorithm is proposed by
introducing the strategy of transfer learning into the process of evidential
clustering. The TECM objective function is constructed by integrating the
knowledge learned from the source domain with the data in the target domain to
cluster the target data. Subsequently, an alternate optimization scheme is
developed to solve the constraint objective function of the TECM algorithm. The
proposed TECM algorithm is applicable to cases where the source and target
domains have the same or different numbers of clusters. A series of experiments
were conducted on both synthetic and real datasets, and the experimental
results demonstrated the effectiveness of the proposed TECM algorithm compared
to ECM and other representative multitask or transfer-clustering algorithms.
| Search Query: ArXiv Query: search_query=au:”Feng Wang”&id_list=&start=0&max_results=10