Spectral Clustering With Adaptive Neighbors for Deep Learning
| العنوان: | Spectral Clustering With Adaptive Neighbors for Deep Learning |
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| المؤلفون: | Yang Zhao, Xuelong Li |
| المصدر: | IEEE Transactions on Neural Networks and Learning Systems. 34:2068-2078 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2023. |
| سنة النشر: | 2023 |
| مصطلحات موضوعية: | Computer Networks and Communications, Computer science, business.industry, Deep learning, Perspective (graphical), Function (mathematics), Machine learning, computer.software_genre, Spectral clustering, Computer Science Applications, Task (project management), ComputingMethodologies_PATTERNRECOGNITION, Artificial Intelligence, Unsupervised learning, Artificial intelligence, Laplacian matrix, Cluster analysis, business, computer, Software |
| الوصف: | Spectral clustering is a well-known clustering algorithm for unsupervised learning, and its improved algorithms have been successfully adapted for many real-world applications. However, traditional spectral clustering algorithms are still facing many challenges to the task of unsupervised learning for large-scale datasets because of the complexity and cost of affinity matrix construction and the eigen-decomposition of the Laplacian matrix. From this perspective, we are looking forward to finding a more efficient and effective way by adaptive neighbor assignments for affinity matrix construction to address the above limitation of spectral clustering. It tries to learn an affinity matrix from the view of global data distribution. Meanwhile, we propose a deep learning framework with fully connected layers to learn a mapping function for the purpose of replacing the traditional eigen-decomposition of the Laplacian matrix. Extensive experimental results have illustrated the competitiveness of the proposed algorithm. It is significantly superior to the existing clustering algorithms in the experiments of both toy datasets and real-world datasets. |
| تدمد: | 2162-2388 2162-237X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8bc4827d8d257c8dbcb5a00e9ea31225 https://doi.org/10.1109/tnnls.2021.3105822 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi.dedup.....8bc4827d8d257c8dbcb5a00e9ea31225 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21622388 2162237X |
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