Machine Learning Combat Learning Notes 5--principal component analysis (PCA)

1.PCA Algorithm Overview introduction of 1.1 PCA algorithm

PCA (Principal Component analysis) is a statistical process that converts a set of observation values of a possible correlation variable into a set of linearly independent variable values by means of an orthogonal transformation, known as the principal component. The number of principal components is less than or equal to the number of original variables. principle of 1.2 PCA algorithm

The essence of PCA is that the original feature can be linearly transformed and mapped to the low dimension space when the original feature is represented as well as possible. advantages and disadvantages of 1.3 PCA algorithm

(1) Advantages:
(2) Disadvantage: 2.PCA algorithm Implementation