Results 21 to 30 of about 3,374,256 (360)
Multi-View Spectral Clustering via ELM-AE Ensemble Features Representations Learning
IEEE Access, 2020 Spectral cluster based on multi-view data has proven effective for clustering multi-source real-world data because consensus and complementary information of multi-view data ensure the result of clustering.
Lijuan Wang, Shifei Ding
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Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap [PDF]
IEEE Signal Processing Letters, 2020 In this study, we propose a new spectral clustering framework that can auto-tune the parameters of the clustering algorithm in the context of speaker diarization. The proposed framework uses normalized maximum eigengap (NME) values to estimate the number
Tae Jin Park +3 more
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Multi-View Spectral Clustering Tailored Tensor Low-Rank Representation [PDF]
IEEE transactions on circuits and systems for video technology (Print), 2020 This paper explores the problem of multi-view spectral clustering (MVSC) based on tensor low-rank modeling. Unlike the existing methods that all adopt an off-the-shelf tensor low-rank norm without considering the special characteristics of the tensor in ...
Yuheng Jia +4 more
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Federated Multi-View Spectral Clustering
IEEE Access, 2020 Multi-view spectral clustering (MVSC) has become a popular approach to harvest knowledge about group information from multiple views of data, owned by different parties. A high quality MVSC approach usually requires collecting massive amount of data from
Hongtao Wang +4 more
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Multi-View Spectral Clustering With High-Order Optimal Neighborhood Laplacian Matrix [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2020 Multi-view spectral clustering can effectively reveal the intrinsic clustering structure among data by performing clustering on the learned optimal embedding across views.
Weixuan Liang +7 more
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Image segmentation by selecting eigenvectors based on extended information entropy
IET Image Processing, 2023 For spectral clustering algorithm, the quality of eigenvectors of graph affinity matrix is very important for the clustering result. So, how to obtain highâquality eigenvectors is crucial.
Daming Zhang, Xueyong Zhang, Huayong Liu
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Accelerated Spectral Clustering Using Graph Filtering Of Random Signals [PDF]
, 2015 We build upon recent advances in graph signal processing to propose a faster spectral clustering algorithm. Indeed, classical spectral clustering is based on the computation of the first k eigenvectors of the similarity matrix' Laplacian, whose ...
Borgnat, Pierre +4 more
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PolSAR Image Classification Method Based on Markov Discriminant Spectral Clustering
Leida xuebao, 2019 Due to the existing spectral clustering methods have low accuracy for PolSAR image classification, a Markov-based Discriminative Spectral Clustering(MDSC) method is proposed, which has the characteristics of low-rank and sparse decomposition.
ZHANG Xiangrong +4 more
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SACOC: A spectral-based ACO clustering algorithm [PDF]
, 2014 The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning
A.P. Dempster +8 more
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Scalable Spectral Clustering With Nyström Approximation: Practical and Theoretical Aspects
IEEE Open Journal of Signal Processing, 2020 Spectral clustering techniques are valuable tools in signal processing and machine learning for partitioning complex data sets. The effectiveness of spectral clustering stems from constructing a non-linear embedding based on creating a similarity graph ...
Farhad Pourkamali-Anaraki
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