Arrow Research search

Author name cluster

Zheng Yan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

8 papers
2 author rows

Possible papers

8

AAAI Conference 2025 Conference Paper

Scalable Federated One-Step Multi-View Clustering with Tensorized Regularization

  • Wei Feng
  • Danting Liu
  • Qianqian Wang
  • Wenqi Liang
  • Zheng Yan

Multi-view clustering (MVC) methods have garnered considerable attention within centralized data frameworks. However, real-world multi-view data are often collected and stored by different organizations, complicating the practical deployment of MVC and motivating the emergence of federated multi-view clustering (FMVC). Existing FMVC approaches typically necessitate post-processing to derive clustering labels and confront challenges in effectively exploring the complementary and consistent information across multi-view data residing in different entities. To address these limitations, we propose a novel framework termed Scalable Federated One-Step Multi-View Clustering with Tensorized Regularization (SFOMVC-TR). This framework facilitates one-step clustering at each client and employs tensor learning to capture consistent and complementary information through a centralized server. Additionally, it adopts anchor graphs to enhance clustering efficiency and scalability in high-dimensional data. By incorporating a Lp,q sparse regularization on the projection matrix, SFOMVC-TR enables the direct projection of anchors into clustering assignments to mitigate redundancy. A federated optimization framework is developed to support collaborative and privacy-preserving training under the coordination of the server. Extensive experiments on multiple datasets validate the privacy and effectiveness of our method.

PDF Details DOI

ICRA Conference 2020 Conference Paper

DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances

  • Tianming Wang
  • Wenjie Lu 0004
  • Zheng Yan
  • Dikai Liu

This paper presents an observer-integrated Reinforcement Learning (RL) approach, called Disturbance OB-server Network (DOB-Net), for robots operating in environments where disturbances are unknown and time-varying, and may frequently exceed robot control capabilities. The DOB-Net integrates a disturbance dynamics observer network and a controller network. Originated from conventional DOB mechanisms, the observer is built and enhanced via Recurrent Neural Networks (RNNs), encoding estimation of past values and prediction of future values of unknown disturbances in RNN hidden state. Such encoding allows the controller generate optimal control signals to actively reject disturbances, under the constraints of robot control capabilities. The observer and the controller are jointly learned within policy optimization by advantage actor critic. Numerical simulations on position regulation tasks have demonstrated that the proposed DOB-Net significantly outperforms conventional feedback controllers and classical RL policy.

Details

JBHI Journal 2020 Journal Article

Guest Editorial: Blockchain and Healthcare Computing

  • Yulei Wu
  • Zheng Yan
  • F. Richard Yu
  • Robert Deng
  • Vijay Varadharajan
  • Wei Chen

The four papers in this special section focus on the use of blockchain in the healthcare field. With the development of society, health has received increasing attentions. The development of science and technology has also promoted the protection of health. In recent years, the rapid development of computing and networking technologies has improved the ability to collect, measure, and analyze health-related data, and thus tremendous opportunities have opened up for healthcare computing. Meanwhile, these technologies have also brought new challenges and issues.

Details DOI