We connect the problem of semi-supervised clustering to constrained Markov aggregation, i.e., the task of partitioning the state space of a Markov chain. We achieve this connection by considering every data point in the dataset as an element of the Markov chain's state space, by defining the transition probabilities between states via similarities between corresponding data points, and by incorporating semi-supervision information as hard constraints in a Hartigan-style algorithm. The introduced Constrained Markov Clustering (CoMaC) is an extension of a recent information-theoretic framework for (unsupervised) Markov aggregation to the semi-supervised case. Instantiating CoMaC for certain parameter settings further generalizes two previous information-theoretic objectives for unsupervised clustering. Our results indicate that CoMaC is competitive with the state-of-the-art.
Co-Clustering via Information-Theoretic Markov Aggregation
Technical University of Munich（慕尼黑工业大学）；Rohde & Schwarz GmbH & Co. KG（罗德与施瓦茨有限两合公司）；Graz University of Technology（格拉茨工业大学）；KNOW-CENTER Gmbh（KNOW-CENTER有限公司）
The Capacity of Information-Theoretic Secure Aggregation in Federated Learning
Information Coding and Transmission (ICT) Key Laboratory of Sichuan Province, Southwest Jiaotong University（四川省信息编码与传输（ICT）重点实验室，西南交通大学）；School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）
Information-Theoretic Decentralized Secure Aggregation with Passive Collusion Resilience
Department of Electrical Engineering and Computer Science, Technical University of Berlin（技术大学柏林电气工程与计算机科学系）；Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University（广西多媒体通信与网络技术重点实验室，广西大学）；School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）；School of Electrical Engineering and Telecommunications, University of New South Wales（新南威尔士大学电子工程与电信学院）
