Results 21 to 30 of about 234,781 (273)
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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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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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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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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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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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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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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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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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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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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