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Mathematics in
2023.07.21
Manifold learning encompasses much of the disciplines of geometry, computation, and statistics, and has become an important research topic in data mining and statistical learning. The simplest description of manifold learning is that it is a class of algorithms for recovering a low-dimensional manifold embedded in a high-dimensional ambient space.
Manifold learning encompasses much of the disciplines of geometry, computation, and statistics, and has become an important research topic in data mining and statistical learning. The simplest description of manifold learning is that it is a class of algorithms for recovering a low-dimensional manifold embedded in a high-dimensional ambient space.
The goal of this mini-course is to introduce several fundamental algorithms in manifold learning: MDS (Multidimensional Scaling), LLE(Locally Linear Embedding), Laplacian Eigenmap, TDA(Topological Data Analysis). We will focus on the theoretical properties of them. The prerequisite of this mini-course is linear algebra.
The goal of this mini-course is to introduce several fundamental algorithms in manifold learning: MDS (Multidimensional Scaling), LLE(Locally Linear Embedding), Laplacian Eigenmap, TDA(Topological Data Analysis). We will focus on the theoretical properties of them. The prerequisite of this mini-course is linear algebra.
Schedule
Schedule
10:00-10:30 Introduction Chih-Wei Chen
10:30-11:30 MDS Seçkin Gunsen
11:30-13:00 Lunch break
13:00-14:30 LLE Liren Lin
14:30-15:30 Laplacian Eigenmap Chin-Hung Jephian Lin
15:30-15:45 Break
15:45-17:15 TDA Yi-Sheng Wang
10:00-10:30 Introduction Chih-Wei Chen
10:30-11:30 MDS Seçkin Gunsen
11:30-13:00 Lunch break
13:00-14:30 LLE Liren Lin
14:30-15:30 Laplacian Eigenmap Chin-Hung Jephian Lin
15:30-15:45 Break
15:45-17:15 TDA Yi-Sheng Wang
Location
Location
Room 4009-1, 4F, College of Science,
NSYSU, Kaohsiung
Room 4009-1, 4F, College of Science,
NSYSU, Kaohsiung
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