Coursera.MachineLearning.Week8

Machine Learning - Week 8 : Unsupervised Learning&Dimensionality Reduction（k-means & PCA）
Ubuntu16.04.1上安装Octave4.0.2

Unsupervised Learning

1. Clustering

1.1 K-means algorithm

input steps

1.2 Optimization objective

loss function J() steps

1.3 Random Initialization

Make K-means avoid local optima——multiple random initializations

Random Initialization initialize some times to avoid local optima when k = 2 to 10

1.4 Choosing the number of clusters【k】——Elbow method

Elbow method tests

Dimensionality Reduction

2. Motivation

2.1 Motivation 1: Data Compression
For example: 2D -> 1D, 3D -> 2D

2.2 Motivation 2: Data Visualization
ND -> 2/3D can visualize it (N >= 2/3).

3. Principal Component Analysis【PCA】

通常需要先将数据归一化

Data preprocessing What PCA do PCA实现 [U,S,V]=svd(Sigma) get U(n*k) PCA Algorithm Summary

4. Applying PCA

4.1 Reconstruction from compressed representation

U reduce

4.2 Choosing the number of principal components

Choosing k

仅调用一次svd()函数，计算不同的k值是否满足>=0.99，获得合适的k参数。

Choosing k method Choosing k in Octave

4.3 Advice for applying PCA

Application of PCA

使用正则化的方法避免过拟合，而非PCA

Bad use of PCA : To prevent overfitting

在已经使用ML算法后发现有必要使用PCA时，再使用

When PCA should be used