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Dynamic Graph Learning for Dimension Reduction and Data Clustering - Farid - 09-30-2023
![[Image: NE2ntq.931pzncv8fee.jpg]](https://i.im.ge/2023/09/30/NE2ntq.931pzncv8fee.jpg)
Dynamic Graph Learning for Dimension Reduction and Data Clustering | 162 | Lei Zhu, Jingjing Li, Zheng Zhang | 2024 | Springer Nature Switzerland |
This book illustrates how to achieve effective dimension reduction and data clustering. The authors explain how to accomplish this by utilizing the advanced dynamic graph learning technique in the era of big data. The book begins by providing background on dynamic graph learning. The authors discuss why it has attracted considerable research attention in recent years and has become well recognized as an advanced technique. After covering the key topics related to dynamic graph learning, the book discusses the recent advancements in the area. The authors then explain how these techniques can be practically applied for several purposes, including feature selection, feature projection, and data clustering.
Contents of Download:
978.3.031.42313.0.epub (17.46 MB)
978.3.031.42313.0.pdf (2.43 MB)
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