Results 11 to 20 of about 1,461,923 (295)
Power Spectral Clustering [PDF]
Journal of Mathematical Imaging and Vision, 2020 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Challa, Aditya +3 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2020 In the past decades, spectral clustering (SC) has become one of the most effective clustering algorithms. However, most previous studies focus on spectral clustering tasks with a fixed task set, which cannot incorporate with a new spectral clustering task without accessing to previously learned tasks.
Gan Sun +4 more
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Semidefinite spectral clustering
Pattern Recognition, 2006 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jaehwan Kim, Seungjin Choi
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Discrete Nonnegative Spectral Clustering
IEEE Transactions on Knowledge and Data Engineering, 2017 Spectral clustering has been playing a vital role in various research areas. Most traditional spectral clustering algorithms comprise two independent stages (e.g., first learning continuous labels and then rounding the learned labels into discrete ones), which may cause unpredictable deviation of resultant cluster labels from genuine ones, thereby ...
Yang, Yang +5 more
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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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On evolutionary spectral clustering [PDF]
ACM Transactions on Knowledge Discovery from Data, 2009 Evolutionary clustering is an emerging research area essential to important applications such as clustering dynamic Web and blog contents and clustering data streams. In evolutionary clustering, a good clustering result should fit the current data well, while simultaneously not deviate too dramatically from the recent history.
Yun Chi +4 more
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Spectral clustering based on the local similarity measure of shared neighbors
ETRI Journal, 2022 Spectral clustering has become a typical and efficient clustering method used in a variety of applications. The critical step of spectral clustering is the similarity measurement, which largely determines the performance of the spectral clustering method.
Zongqi Cao, Hongjia Chen, Xiang Wang
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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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Multidimensional partitioning and bi-partitioning : analysis and application to gene expression datasets [PDF]
, 2008 Eigenvectors and, more generally, singular vectors, have proved to be useful tools for data mining and dimension reduction. Spectral clustering and reordering algorithms have been designed and implemented in many disciplines, and they can be motivated ...
Gabriela Kalna +8 more
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