71 citations · 160 across the 11 of their papers we have counts for
6 papers · 1 filter
Learnable Weighting of Intra-Attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes
Yiqun Zhang, Yiu-ming Cheung
The success of categorical data clustering generally much relies on the distance metric that measures the dissimilarity degree between two objects. However, most of the existing cl…
Federated Semi-Supervised Learning with Annotation Heterogeneity
Xinyi Shang, Gang Huang, Yang Lu +4
Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data. Most of the existing FSSL work…
Uniform tensor clustering by jointly exploring sample affinities of various orders
Hongmin Cai, Fei Qi, Junyu Li +4
Conventional clustering methods based on pairwise affinity usually suffer from the concentration effect while processing huge dimensional features yet low sample sizes data, result…
Label-Noise Learning with Intrinsically Long-Tailed Data
Yang Lu, Yiliang Zhang, Bo Han +2
Label noise is one of the key factors that lead to the poor generalization of deep learning models. Existing label-noise learning methods usually assume that the ground-truth class…
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation
Xinyi Shang, Yang Lu, Yiu-ming Cheung +1
Federated learning provides a privacy guarantee for generating good deep learning models on distributed clients with different kinds of data. Nevertheless, dealing with non-IID dat…
Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data
Yiu-ming Cheung, Juyong Jiang, Feng Yu +1
Despite enormous research interest and rapid application of federated learning (FL) to various areas, existing studies mostly focus on supervised federated learning under the horiz…