版权声明:本文为博主原创文章,未经博主允许不得转载。 https://blog.csdn.net/sinat_35406909/article/details/82319095
1. Task of PCA
Find a direction and project all points to that line, thus minimizing the projection error.
Projection error: Sum of distances between points and line
2. Data Preprocessing
Feature Scaling + Mean Normalization
3. PCA Algorithm
Using the first k vectors in
and denote it as
, the result is
4. Reconstruction from PCA
5. How to Choose the Reduced Dimension
Using
, Check whether
6. Speed Up Supervised Learning by PCA
Train the model using data compressed by PCA
Note: Running PCA which only depends on TRAINING SET when training!
While this mapping can be applied to other sets.