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LiteWSC: A Lightweight Framework for Web-Scale Spectral Clustering
Yang, Geping1; Deng, Sucheng2; Yang, Yiyang1; Gong, Zhiguo2; Chen, Xiang3; Hao, Zhifeng4
2022-04-11
Conference NameDASFAA2022
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13246 LNCS
Pages556-573
Conference Date2022-04-11
Conference PlaceHyderabad, India
Abstract

Spectral clustering is an effective clustering method for its excellent performance in partitioning non-linearly distributed data. Nevertheless, it suffers from scalability due to its high computational complexity in constructing the Laplacian graph and computing the corresponding eigendecomposition. In the past decades, many efforts have been made to face this problem. However, the computational bottleneck is still a problem in processing extensive data, especially in web-scale scenarios. We present LiteWSC, a simple yet efficient lightweight spectral clustering framework, to cluster web-scale data with limited resource requirements. Our framework has minimal space overhead and does not require explicit overall embeddings computation. We also analyze the theoretical guarantee and performance boundary of LiteWSC. LiteWSC is highly flexible with O(sp) memory requirement, where s and p are the number of samples and the number of prototypes, which are adaptive to the available resource. Therefore, LiteWSC can partition web-scale data (e.g., n= 8, 000 k ) in an resource-limited host (e.g., memory is restricted to 1 GB). Experiments on real-world, large-scale and web-scale datasets demonstrate both the efficiency and effectiveness of LiteWSC over state-of-the-art methods.

KeywordData Quantization Machine Learning Scalability Spectral Clustering
DOI10.1007/978-3-031-00126-0_40
URLView the original
Language英語English
Scopus ID2-s2.0-85129848898
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Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Faculty of Computer, Guangdong University of Technology, Guangzhou, China
2.State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau, Macao
3.School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou, China
4.College of Engineering, Shantou University, Shantou, China
Recommended Citation
GB/T 7714
Yang, Geping,Deng, Sucheng,Yang, Yiyang,et al. LiteWSC: A Lightweight Framework for Web-Scale Spectral Clustering[C],2022:556-573.
APA Yang, Geping,Deng, Sucheng,Yang, Yiyang,Gong, Zhiguo,Chen, Xiang,&Hao, Zhifeng.(2022).LiteWSC: A Lightweight Framework for Web-Scale Spectral Clustering.Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),13246 LNCS,556-573.
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