===== 2023 Winter Reading Group =====

**Time**: Friday 10am 

**Topic**: manifold learning basics and applications   

[[reading:instruction|讨论简要说明]] 

==== Schedule ==== 
^Date	^Presenter^ Reading ^ Slides/Notes ^
|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   | Zhiyuan Peng & Dexu Kong|   A1. Chapter 4.4-4.5       |        |
|1.27.  |        |   B1       |        |
|2.3.   |        |   B2       |        |
|2.10   |        |    B3      |        |
|2.17   |        |.         |        |

==== 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://people.cs.uchicago.edu/~niyogi/papersps/BNcolt05.pdf| Towards a Theoretical Foundation for Laplacian-Based Manifold Methods]]. (B1)
  - [[https://arxiv.org/pdf/1902.01738.pdf|Metric Learning on Manifolds]] (B2)
  - [[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..