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Selective Update of Relevant Eigenspaces for Integrative clustering of Multimodal Data

IEEE TRANSACTIONS ON CYBERNETICS 2022年第2期52卷 947-959页

作者： Khan, Aparajita Maji, Pradipta Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata 700108 India

One of the major problems in cancer subtype discovery from multimodal omic data is that all the available modalities may not encode relevant and homogeneous information about the subtypes. Moreover, the high-dimensional nature of the modalities makes sample clustering computationally expensive. In this regard, a novel algorithm is proposed to extract a low-rank joint subspace of the integrated data matrix. The proposed algorithm first evaluates the quality of subtype information provided by each of the modalities, and then judiciously selects only relevant ones to construct the joint subspace. The problem of incrementally updating the singular value decomposition of a data matrix is formulated for the multimodal data framework. The analytical formulation enables efficient construction of the joint subspace of integrated data from low-rank subspaces of the individual modalities. The construction of joint subspace by the proposed method is shown to be computationally more efficient compared to performing the principal component analysis (PCA) on the integrated data matrix. Some new quantitative indices are introduced to measure theoretically the accuracy of subspace construction by the proposed approach with respect to the principal subspace extracted by the PCA. The efficacy of clustering on the joint subspace constructed by the proposed algorithm is established over existing integrative clustering approaches on several real-life multimodal cancer data sets.

关键词： clustering algorithms Bioinformatics Genomics Principal component analysis Cancer Covariance matrices Data models Integrative clustering low-rank approximation modality selection singular value decomposition (SVD)

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Memory and Communication Efficient Federated Kernel k-Means

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第5期35卷 7114-7125页

作者： Zhou, Xiaochen Wang, Xudong Shanghai Jiao Tong Univ Univ Michigan Shanghai Jiao Tong Univ Joint Inst Shanghai 200240 Peoples R China

A federated kernel k -means (FedKKM) algorithm is developed in this article to conduct distributed clustering with low memory consumption on user devices. In FedKKM, a federated eigenvector approximation (FEA) algorithm is designed to iteratively determine the low-dimensional approximate vectors of the transformed feature vectors, using only low-dimensional random feature vectors. To maintain high communication efficiency in each iteration of FEA, a communication-efficient Lanczos algorithm (CELA) is further designed in FEA to reduce the communication cost. Based on the low-dimensional approximate vectors, the clustering result is obtained by leveraging a distributed linear k -means algorithm. A theoretical analysis shows that: 1) FEA has a convergence rate of O(1/T) , where T is the number of iterations;2) the scalability of FedKKM is not affected by the dataset size since the communication cost of FedKKM is independent of the number of users' data;and 3) FedKKM is a (1+epsilon) approximation algorithm. The experimental results show that FedKKM achieves the comparable clustering quality to that of a centralized kernel k -means. Compared with state-of-the-art schemes, FedKKM reduces the memory consumption on user devices by up to 94% and also reduces the communication cost by more than 40%.

关键词： Kernel clustering algorithms Approximation algorithms Servers Costs Memory management Training clustering communication efficiency federated learning (FL) kernel k-means memory consumption

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Optimal Channel Selection and Switching Using Q-Learning in Cognitive Radio Ad Hoc Networks

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第3期70卷 6314-6326页

作者： Srivastava, Anushree Pal, Raghavendra Prakash, Arun Tripathi, Rajeev Gupta, Nishu Alkhayyat, Ahmed Motilal Nehru Natl Inst Technol Allahabad Dept Elect & Commun Engn Prayagraj 211004 India Sardar Vallabhbhai Natl Inst Technol Dept Elect Engn Surat 395007 India VTT Tech Res Ctr Finland Ltd Future Commun Networks Res Unit Oulu 90590 Finland Islamic Univ Comp Engn Technol Dept Najaf 54001 Iraq

With the rising demand for spectrum and the emergence of advanced communication systems, there is a critical requirement for more efficient and streamlined approaches to spectrum utilization. Thus, suitable frequency channel allocation and switching techniques in Cognitive radio (CR) are essential for increasing the spectrum utilization efficiency. Although researchers have been working in this area for a demi-decade, the chances of the collision of primary user and secondary user transmission are still not reduced to zero. To further reduce this problem, the authors in this article have proposed an optimal channel selection and switching strategy for cognitive radio ad hoc networks (CRAHNs) seeking maximum reward for a particular channel using Q-learning algorithm in combination with clustering algorithm. For data transmission, channel with the largest Q-value is chosen. Through extensive simulations and comparative analysis, it can be seen that in comparison to the latest existing scheme, the proposed QLOCA scheme improves packet delivery ratio by 3.8%, throughput is improved by 6.454%, average delay is reduced by 7.2% and packet collision ratio is reduced by 4.2%.

关键词： Switches Q-learning Throughput clustering algorithms Cognitive radio Channel allocation Simulation Channel selection channel switching cognitive radio networks

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Semi-Supervised Structured Subspace Learning for Multi-View clustering

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 1-14页

作者： Qin, Yalan Wu, Hanzhou Zhang, Xinpeng Feng, Guorui Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China

Multi-view clustering aims at simultaneously obtaining a consensus underlying subspace across multiple views and conducting clustering on the learned consensus subspace, which has gained a variety of interest in image processing. In this paper, we propose the Semi-supervised Structured Subspace Learning algorithm for clustering data points from Multiple sources (SSSL-M). We explicitly extend the traditional multi-view clustering with a semi-supervised manner and then build an anti-block-diagonal indicator matrix with small amount of supervisory information to pursue the block-diagonal structure of the shared affinity matrix. SSSL-M regularizes multiple view-specific affinity matrices into a shared affinity matrix based on reconstruction through a unified framework consisting of backward encoding networks and the self-expressive mapping. The shared affinity matrix is comprehensive and can flexibly encode complementary information from multiple view-specific affinity matrices. An enhanced structural consistency of affinity matrices from different views can be achieved and the intrinsic relationships among affinity matrices from multiple views can be effectively reflected in this manner. Technically, we formulate the proposed model as an optimization problem, which can be solved by an alternating optimization scheme. Experimental results over seven different benchmark datasets demonstrate that better clustering results can be obtained by our method compared with the state-of-the-art approaches.

关键词： Image reconstruction Image coding clustering algorithms Deep learning clustering methods Unsupervised learning Sparse matrices Multi-view clustering anti-block-diagonal indicator matrix semi-supervised learning alternating optimization scheme enhanced structural consistency

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Latency-Constrained Multi-User Efficient Task Scheduling in Large-Scale Internet of Vehicles

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第10期23卷 9821-9834页

作者： Ma, Buyun Ren, Zhiyuan Cheng, Wenchi Wang, Jingqing Zhang, Wei Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Driven by the tremendous demand for real-time data processing in the Internet of Vehicles (IoV), edge computing is envisioned as a promising solution to alleviate the resource limitation on vehicles. Current works on edge task scheduling simply optimize the total system cost and ignore the various constraints of applications, which will result in the reduction of the task completion rate and even cause security accidents. Although few works studied the multi-task deadline-constrained scheduling problem, their complexity is too high, resulting in the explosive growth of the runtime. Spurred by the above issues, the multi-task scheduling problem is formulated to maximize the task completion rate. Further, a Multi-user Efficient Task Scheduling (METS) algorithm is proposed to solve the formulated problem, which consists of three key components: 1) the dominating set-based network clustering that aims to reduce the network scale, 2) the matching-based task assignment to assign tasks that are modeled by the Directed Acyclic Graph (DAG) to their proper clusters, and 3) the intra-cluster DAG scheduling to schedule DAGs to the proper network nodes. Simulation results show that the proposed METS algorithm can significantly improve the task completion rate and reduce the algorithm runtime in an IoV environment with thousand-level network scale and thousand-level task requests.

关键词： Task analysis Scheduling Processor scheduling Multitasking clustering algorithms Energy consumption Costs Edge computing latency constraint multi-user task offloading

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conteNXt: A Graph-Based Approach to Assimilate Content and Context for Event Detection in OSN

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第4期11卷 5483-5495页

作者： Sharma, Sielvie Abulaish, Muhammad Ahmad, Tanvir Jamia Millia Islamia Dept Comp Engn New Delhi 110025 India South Asian Univ Dept Comp Sci New Delhi 110068 India

Social networks are rapidly expanding due to their imperative role in disseminating information in a split second, emerging as the primary source for breaking news. As a result, the rich, user-generated information entices researchers to delve deeper and extract valuable insights. Event detection in online social networks (OSNs) is a research problem that has shifted researchers attention from traditional news media to online social media data. Event detection in OSNs is an automated process, addressing the impractical task of manually filtering potential events from vast amounts of online data. Unfortunately, the informality and semantic sparsity of online social networking text pose significant challenges to the event detection task. To this end, we present an approach named conteNXt for detecting events from Twitter (currently "X") posts (also known as Tweets). To handle large amounts of data, the proposed method divides tweets into bins and uses postprocessing methods to extract bursty keyphrases. These keyphrases are then used to generate a weighted keyphrase graph using the Word2Vec model. Finally, Markov clustering is employed to cluster and detect events in the bursty keyphrase graph. conteNXt is evaluated on the Event-Corpus2012 benchmark dataset and two additional datasets extracted from the archive, Archive2020 and Archive2021, using performance evaluation metrics: #events, precision, recall, and F1-score. The proposed approach outperforms state-of-the-art methods, including SEDTWik, Twevent, Sentence-BERT, MABED, EDED, CommunityINDICATOR, and EventX. Additionally, the proposed approach is capable of detecting vital events that are not identified by the aforementioned state-of-the-art methods.

关键词： Event detection Social networking (online) Blogs Semantics Feature extraction Media clustering algorithms Bursty keyphrases event detection Markov clustering (MCL) online social network (OSN) social network analysis

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A One-Shot Framework for Distributed Clustered Learning in Heterogeneous Environments

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 636-651页

作者： Armacki, Aleksandar Bajovic, Dragana Jakovetic, Dusan Kar, Soummya Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA Univ Novi Sad Fac Tech Sci Novi Sad 21000 Serbia Univ Novi Sad Fac Sci Novi Sad 21000 Serbia

The paper proposes a family of communication efficient methods for distributed learning in heterogeneous environments in which users obtain data from one of K different distributions. In the proposed setup, the grouping of users (based on the data distributions they sample), as well as the underlying statistical properties of the distributions, are apriori unknown. A family of One-shot Distributed Clustered Learning methods (ODCL- C ) is proposed, parametrized by the set of admissible clustering algorithms C , with the objective of learning the true model at each user. The admissible clustering methods include K -means (KM) and convex clustering (CC), giving rise to various one-shot methods within the proposed family, such as ODCL-KM and ODCL-CC. The proposed one-shot approach, based on local computations at the users and a clustering based aggregation step at the server is shown to provide strong learning guarantees. In particular, for strongly convex problems it is shown that, as long as the number of data points per user is above a threshold, the proposed approach achieves order-optimal mean squared error (MSE) rates in terms of the sample size. An explicit characterization of the threshold is provided in terms of problem parameters. The trade-offs with respect to selecting various clustering methods (ODCL-CC, ODCL-KM) are discussed and significant improvements over state-of-the-art are demonstrated. Numerical experiments illustrate the findings and corroborate the performance of the proposed methods.

关键词： clustering algorithms Computational modeling Signal processing algorithms Training Costs Statistics Sociology Distributed learning communication efficiency one-shot methods statistical heterogeneity clustered learning mean-squared analysis order-optimality

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Research on Intelligent clustering Scoring of English Text Based on XGBOOST Algorithm 14th

Research on Intelligent Clustering Scoring of English Text B...

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14th International Symposium on Intelligence Computation and Applications, ISICA 2023

作者： Zeng, Zhaolian Yao, Wanyi Zeng, Jia Lei, Jiawei Chen, Feiyun Wen, Peihua School of Foreign Studies South China Agricultural University Guangzhou510642 China College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China

ISBN: (纸本)9789819743957

In today's explosive growth of online text data, how to feature mine text data from a wide range of sources, cluster text data with similar features, and classify them according to their features has become a hot issue. In this paper, according to the characteristics of English text data, considering the existence of a large number of redundant features in English text, the existence of features with similar meanings in English text and other problems, the following method is proposed: in the first step, the English text is segmented, data preprocessing is carried out, and then the text data is processed by using the TF-IDF algorithm, to get the roughly selected subset of features;in the second step, the improved Binary Particle Swarm Optimization algorithm is used to In the second step, the improved binary particle swarm optimization algorithm GSBPSO (Global-to-local Searching-based BinaryParticle Swarm Optimization) is used to re-select the features of the roughly selected feature subset obtained in the first step to obtain the optimal feature subset;in the third step, the binary SKM clustering algorithm is used to cluster English text according to the optimal feature subset obtained in the second step. English text is clustered according to the optimal feature subset obtained in the second step, and the classification criteria are formulated for the existing text corpus to categorize the text data. Finally, the XGBOOST algorithm is applied to evaluate the prediction and establish the intelligent clustering scoring model of English text based on XGBOOST. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： clustering algorithms

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Robust Access Point clustering in Edge Computing Resource Optimization

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第3期19卷 2738-2750页

作者： Yellas, Nour-El-Houda Boumerdassi, Selma Ceselli, Alberto Maaz, Bilal Secci, Stefano Cnam Ctr Studies & Res Comp Sci & Commun CEDRIC F-75141 Paris France Univ Milan Dept Informat I-20122 Milan Italy

Multi-access Edge Computing (MEC) technology has emerged to overcome traditional cloud computing limitations, challenged by the new 5G services with heavy and heterogeneous requirements on both latency and bandwidth. In this work, we tackle the problem of clustering access points in MEC environments, introducing a set of clustering models to be deployed at the pre-provisioning phase. We go through extensive simulations on real-world traffic demands to evaluate the performance of the proposed solutions. In addition, we show how MEC hosts capacity violation can be decreased when integrating access points clustering into the orchestration model, by investigating on solution accuracy when applied on held-out users traffic demands. The obtained results show that our approach outperforms two state-of-the-art algorithms, reducing both memory usage and execution time, by 46% and 50%, respectively, in comparison to a baseline algorithm. It surpasses the two methods in gaining control over MEC hosts capacity usage for different maximum achieved occupancy levels on MEC hosts.

关键词： Virtualization clustering algorithms 5G mobile communication Task analysis Robustness Computer architecture Computational modeling Multi-access edge computing RAN virtualization access point clustering MEC resource allocation

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DNB: A Joint Learning Framework for Deep Bayesian Nonparametric clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第12期33卷 7610-7620页

作者： Wang, Zeya Ni, Yang Jing, Baoyu Wang, Deqing Zhang, Hao Xing, Eric Petuum Inc Pittsburgh PA 15222 USA Texas A&M Univ Dept Stat College Stn TX 77843 USA Univ Illinois Dept Comp Sci Champaign IL 61820 USA Beihang Univ Sch Comp Sci Beijing 100083 Peoples R China

clustering algorithms based on deep neural networks have been widely studied for image analysis. Most existing methods require partial knowledge of the true labels, namely, the number of clusters, which is usually not available in practice. In this article, we propose a Bayesian nonparametric framework, deep nonparametric Bayes (DNB), for jointly learning image clusters and deep representations in a doubly unsupervised manner. In doubly unsupervised learning, we are dealing with the problem of ``unknown unknowns,'' where we estimate not only the unknown image labels but also the unknown number of labels as well. The proposed algorithm alternates between generating a potentially unbounded number of clusters in the forward pass and learning the deep networks in the backward pass. With the help of the Dirichlet process mixtures, the proposed method is able to partition the latent representations space without specifying the number of clusters a priori. An important feature of this work is that all the estimation is realized with an end-to-end solution, which is very different from the methods that rely on post hoc analysis to select the number of clusters. Another key idea in this article is to provide a principled solution to the problem of ``trivial solution'' for deep clustering, which has not been much studied in the current literature. With extensive experiments on benchmark datasets, we show that our doubly unsupervised method achieves good clustering performance and outperforms many other unsupervised image clustering methods.

关键词： clustering algorithms Task analysis Unsupervised learning Training Neural networks Generative adversarial networks Feature extraction Bayesian nonparametrics (BNPs) convolutional neural network (CNN) image clustering joint learning regularization

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