Affinity propagation is an exemplar-based clustering algorithm that finds a set of data-points that best exemplify the data, and associates each datapoint with one exemplar. We extend affinity propagation in a principled way to solve the hierarchical clustering problem, which arises in a variety of domains including biology, sensor networks and decision making in operational research. We derive an inference algorithm that operates by propagating information up and down the hierarchy, and is efficient despite the high-order potentials required for the graphical model formulation.
We demonstrate that our method outperforms greedy techniques that cluster one layer at a time. We show that on an artificial dataset designed to mimic the HIV-strain mutation dynamics, our method outperforms related methods. For real HIV sequences, where the ground truth is not available, we show our method achieves better results, in terms of the underlying objective function, and show the results correspond meaningfully to geographical location and strain subtypes. Finally we report results on using the method for the analysis of mass spectra, showing it performs favorably compared to state-of-the-art methods.
HiF-DTA: Hierarchical Feature Learning Network for Drug-Target Affinity Prediction
School of Software Engineering Huazhong University of Science（软件工程学院 华中科技大学）；Faculty of Data Science City University of Macau Macau, China（数据科学学院 澳门城市大学）；Union Hospital, Tongji Medical College Huazhong University of Science（同济医学院附属医院 华中科技大学）
Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition
LIPN-UMR CNRS 7030, Univ. Sorbonne Paris Nord（巴黎第十三大学（索邦巴黎北部大学）LIPN-UMR CNRS 7030实验室）；Léonard de Vinci Pôle Universitaire, Research Center（达芬奇大学联盟研究中心）
Cross-Domain Label Propagation for Domain Adaptation with Discriminative Graph Self-Learning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Nanjing University of Science and Technology（南京理工大学）
Biology-inspired joint distribution neurons based on Hierarchical Correlation Reconstruction allowing for multidirectional propagation of values and densities
Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments
College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Tongji Hospital, Tongji University（同济大学附属同济医院）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织Data61研究院）；Guangzhou Huatu Information Technology Co., Ltd.（广州华图信息技术有限公司）；School of Electrical and Computer Engineering, The University of Sydney（悉尼大学电气与计算机工程学院）
