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meeting_2022winter [2023/01/09 21:11] – created yangmeeting_2022winter [2023/01/13 22:33] (current) – yang
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 **Topic**: manifold learning basics and applications    **Topic**: manifold learning basics and applications   
 +
 +[[reading:instruction|讨论简要说明]] 
  
 ==== Schedule ====  ==== Schedule ==== 
 ^Date ^Presenter^ Reading ^ Slides/Notes ^ ^Date ^Presenter^ Reading ^ Slides/Notes ^
-|1.13   |        |   A1. Chapter 4.1-4.3       |        |  +|1.13   |  Jiahao Lai             |   A1. Chapter 4.1-4.3       |   {{ :meeting_2023winter:introduction.pdf |}}  [[https://meeting.tencent.com/v2/cloud-record/share?id=db6e586a-5230-4b6f-95b1-757081adf02f&from=3 | recording]]   |  
-|1.20   |        |   A1. Chapter 4.4-4.5       |        |+|1.20   | Zhiyuan Peng & Dexu Kong|   A1. Chapter 4.4-4.5       |        |
 |1.27.  |        |   B1       |        | |1.27.  |        |   B1       |        |
 |2.3.   |        |   B2       |        | |2.3.   |        |   B2       |        |
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 ==== Reading material ==== ==== Reading material ====
  
-  - [[https://link.springer.com/book/10.1007/978-1-84882-312-9|“Manifold Learning”. Statistical Learning
and Pattern Analysis for Image and Video Processing]], Chapter 4,Nanning Zheng and Jianru Xue (A1)+  - [[https://link.springer.com/book/10.1007/978-1-84882-312-9|“Manifold Learning”. Statistical Learning 
and Pattern Analysis for Image and Video Processing]], Chapter 4,Nanning Zheng and Jianru Xue (A1)
   - [[   - [[
 https://people.cs.uchicago.edu/~niyogi/papersps/BNcolt05.pdf| Towards a Theoretical Foundation for Laplacian-Based Manifold Methods]]. (B1) https://people.cs.uchicago.edu/~niyogi/papersps/BNcolt05.pdf| Towards a Theoretical Foundation for Laplacian-Based Manifold Methods]]. (B1)
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   - [[https://arxiv.org/abs/2003.13913|Flows for simultaneous manifold learning and density estimation]] (B3)   - [[https://arxiv.org/abs/2003.13913|Flows for simultaneous manifold learning and density estimation]] (B3)
  
 +=== Other paper candidates === 
 +
 +  * [[https://arxiv.org/abs/2011.01307|The mathematical foundations of mannifold learning]] 
 +  * Grassmann manifold: 
 +     * [[https://arxiv.org/pdf/1808.02229v1.pdf|Grassmannian Learning: Embedding Geometry Awareness in Shallow and Deep Learning]], 
 +     * [[https://merl.com/publications/docs/TR2012-092.pdf|A grassmann manifold-based domain adaptation approach]]
 +     * [[https://arxiv.org/pdf/1507.07830.pdf| Zero-Shot Domain Adaptation via Kernel Regression on the Grassmannian]]
 +  * More to add..
  
  
  
meeting_2022winter.1673316716.txt.gz · Last modified: 2023/01/09 21:11 by yang