activity
20152022
most citedMedian evidential c-means algorithm and its application to community detection

83 citations · 225 across the 7 of their papers we have counts for

collaborators

7 papers

eess.SY2022★ 27 cited

Multi-sensor Suboptimal Fusion Student's Filter

Tiancheng Li, Zheng Hu, Zhunga Liu +1

A multi-sensor fusion Student's filter is proposed for time-series recursive estimation in the presence of heavy-tailed process and measurement noises. Driven from an informati…

cs.LG2021★ 29 cited

TECM: Transfer Learning-based Evidential C-Means Clustering

Lianmeng Jiao, Feng Wang, Zhun-ga Liu +1

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, bu…

cs.CV2021

Pose Discrepancy Spatial Transformer Based Feature Disentangling for Partial Aspect Angles SAR Target Recognition

Zaidao Wen, Jiaxiang Liu, Zhunga Liu +1

This letter presents a novel framework termed DistSTN for the task of synthetic aperture radar (SAR) automatic target recognition (ATR). In contrast to the conventional SAR ATR alg…

cs.LG2020★ 54 cited

EGMM: an Evidential Version of the Gaussian Mixture Model for Clustering

Lianmeng Jiao, Thierry Denoeux, Zhun-ga Liu +1

The Gaussian mixture model (GMM) provides a simple yet principled framework for clustering, with properties suitable for statistical inference. In this paper, we propose a new mode…

cs.AI2016★ 31 cited

ECMdd: Evidential c-medoids clustering with multiple prototypes

Kuang Zhou, Arnaud Martin, Quan Pan +1

In this work, a new prototype-based clustering method named Evidential C-Medoids (ECMdd), which belongs to the family of medoid-based clustering for proximity data, is proposed as…

cs.AI2015★ 1 cited

Evidential relational clustering using medoids

Kuang Zhou, Arnaud Martin, Quan Pan +1

In real clustering applications, proximity data, in which only pairwise similarities or dissimilarities are known, is more general than object data, in which each pattern is descri…