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Description
Description
Prace dimension reduction of data by using at least 5 algorithms. Suggested algorithms are PCA, kernel PCA, t-SNE, LLE, and Isomap. You have to use at least one Scikit-Learn toy dataset and at least one real-world dataset in this project. Compare and analyze the results among several algorithms.
Prace dimension reduction of data by using at least 5 algorithms. Suggested algorithms are PCA, kernel PCA, t-SNE, LLE, and Isomap. You have to use at least one Scikit-Learn toy dataset and at least one real-world dataset in this project. Compare and analyze the results among several algorithms.
Readings
Readings
Ch9 and Ch10, Machine Learning with Python Cookbook 2nd, by Kyle Gallatin and Chris Albon, O'Reilly, 2023. Free O'Reilly Learning by FJU ID
PCA
A.I 人工智慧 - 課程 18 - machine learning- 流形學習 Manifold Learning, Youtube 2019.
Dimensionality reduction with PCA: from basic ideas to full derivation. 2020. (原理無程式碼)
Principal Component Analysis(PCA) with code on MNIST dataset, Medium, 2019.
范叶亮：特征值分解，奇异值分解和主成份分析 (EVD, SVD and PCA)，流形学习 (Manifold Learning) 2018
主成分分析降维（MNIST数据集）, 2017. Python/Tensorflow
主成份分析(PCA)最详细和全面的诠释, 2016.
Kaggle:使用MNIST数据集进行PCA降维和LDA降维, 2018.
Interactive Intro to Dimensionality Reduction, Kaggle, 2017.
Manifold Learning
在Python中使用PCA和t-SNE可視化高維數據集, 2021.
流形学习t-SNE，LLE，Isomap, 2020.
2020機器學習t-SNE, 2020.
机器学习之降维, 2020.
流行学习-实现高维数据的降维与可视化, 2018. Python/Scikit-Learn code@GitHub 10多種降維方法/MNIST
手写数字上的流形学习: LLE, Isomap, MLLE, t-SNE, Python/Scikit-Learn code, MNIST
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