Results 31 to 40 of about 3,374,256 (360)
Density Gain-Rate Peaks for Spectral Clustering
IEEE Access, 2021 Clustering has been troubled by varying shapes of sample distributions, such as line and spiral shapes. Spectral clustering and density peak clustering are two feasible techniques to address this problem, and have attracted much attention from academic ...
Jiexing Liu, Chenggui Zhao
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Consistency of spectral clustering in stochastic block models [PDF]
, 2014 We analyze the performance of spectral clustering for community extraction in stochastic block models. We show that, under mild conditions, spectral clustering applied to the adjacency matrix of the network can consistently recover hidden communities ...
Lei, Jing, Rinaldo, Alessandro
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Mini-batch spectral clustering [PDF]
2017 International Joint Conference on Neural Networks (IJCNN), 2017 The cost of computing the spectrum of Laplacian matrices hinders the application of spectral clustering to large data sets. While approximations recover computational tractability, they can potentially affect clustering performance. This paper proposes a practical approach to learn spectral clustering based on adaptive stochastic gradient optimization.
Han, Yufei, Filippone, Maurizio
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Deep Spectral Clustering Using Dual Autoencoder Network [PDF]
Computer Vision and Pattern Recognition, 2019 The clustering methods have recently absorbed even-increasing attention in learning and vision. Deep clustering combines embedding and clustering together to obtain optimal embedding subspace for clustering, which can be more effective compared with ...
Xu Yang +4 more
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Complexity, 2021 Abnormal behavior detection of social security funds is a method to analyze large-scale data and find abnormal behavior. Although many methods based on spectral clustering have achieved many good results in the practical application of clustering, the ...
Yan Wu, Yonghong Chen, Wenhao Ling
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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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Fast kernel spectral clustering [PDF]
Neurocomputing, 2017 Abstract Spectral clustering suffers from a scalability problem in both memory usage and computational time when the number of data instances N is large. To solve this issue, we present a fast spectral clustering algorithm able to effectively handle millions of datapoints at a desktop PC scale.
Langone, Rocco, Suykens, Johan
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Identifying cell types from single-cell data based on similarities and dissimilarities between cells
BMC Bioinformatics, 2021 Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research.
Yuanyuan Li +3 more
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Impact of regularization on Spectral Clustering [PDF]
, 2014 The performance of spectral clustering can be considerably improved via regularization, as demonstrated empirically in Amini et. al (2012). Here, we provide an attempt at quantifying this improvement through theoretical analysis.
Joseph, Antony, Yu, Bin
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Affinity Matrix Learning Via Nonnegative Matrix Factorization for Hyperspectral Imagery Clustering
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 In this article, we integrate the spatial-spectral information of hyperspectral image (HSI) samples into nonnegative matrix factorization (NMF) for affinity matrix learning to address the issue of HSI clustering.
Yao Qin +5 more
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