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Ultra-Scalable Spectral Clustering and Ensemble Clustering [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2020 This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC).
Dong Huang +4 more
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An improved multi-view spectral clustering based on tissue-like P systems [PDF]
Scientific Reports, 2022 Multi-view spectral clustering is one of the multi-view clustering methods widely studied by numerous scholars. The first step of multi-view spectral clustering is to construct the similarity matrix of each view.
Huijian Chen, Xiyu Liu
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Covariate-assisted spectral clustering [PDF]
Biometrika, 2017 28 pages, 4 figures, includes substantial changes to theoretical ...
Binkiewicz, Norbert +2 more
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Spectral Embedded Deep Clustering [PDF]
Entropy, 2019 We propose a new clustering method based on a deep neural network. Given an unlabeled dataset and the number of clusters, our method directly groups the dataset into the given number of clusters in the original space.
Yuichiro Wada +5 more
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Spectral Clustering Community Detection Algorithm Based on Point-Wise Mutual Information Graph Kernel [PDF]
Entropy, 2023 To address the problem that traditional spectral clustering algorithms cannot obtain the complete structural information of networks, this paper proposes a spectral clustering community detection algorithm, PMIK-SC, based on the point-wise mutual ...
Yinan Chen, Wenbin Ye, Dong Li
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Spectral clustering with imbalanced data [PDF]
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 Spectral clustering (SC) and graph-based semi-supervised learning (SSL) algorithms are sensitive to how graphs are constructed from data. In particular if the data has proximal and unbalanced clusters these algorithms can lead to poor performance on well-known graphs such as $k$-NN, full-RBF, $ $-graphs.
Qian, Jing, Saligrama, Venkatesh
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Kernel Spectral Clustering and Applications [PDF]
, 2016 In this chapter we review the main literature related to kernel spectral clustering (KSC), an approach to clustering cast within a kernel-based optimization setting. KSC represents a least-squares support vector machine based formulation of spectral clustering described by a weighted kernel PCA objective.
Langone, Rocco +3 more
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Fault Diagnosis by Multisensor Data: A Data-Driven Approach Based on Spectral Clustering and Pairwise Constraints [PDF]
Sensors, 2020 This paper deals with clustering based on feature selection of multisensor data in high-dimensional space. Spectral clustering algorithms are efficient tools in signal processing for grouping datasets sampled by multisensor systems for fault diagnosis ...
Massimo Pacella, Gabriele Papadia
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Fair Method for Spectral Clustering to Improve Intra-cluster Fairness [PDF]
Jisuanji kexue, 2023 Recently,the fairness of the algorithm has aroused extensive discussion in the machine learning community.Given the widespread popularity of spectral clustering in modern data science,studying the algorithm fairness of spectral clustering is a crucial ...
XU Xia, ZHANG Hui, YANG Chunming, LI Bo, ZHAO Xujian
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Quantum spectral clustering [PDF]
Physical Review A, 2021 Spectral clustering is a powerful unsupervised machine learning algorithm for clustering data with non convex or nested structures. With roots in graph theory, it uses the spectral properties of the Laplacian matrix to project the data in a low-dimensional space where clustering is more efficient.
Iordanis Kerenidis, Jonas Landman
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