Pca technology

PCA algorithm

Collection

zero Useful+1

zero

synonym principal component analysis (Statistical method) generally refers to pca technology

I.e principal component analysis Technology, also known as principal component analysis technology, aims to use Dimension reduction The idea of Multiple indicators Into a few Comprehensive indicators 。

Chinese name: Pca technology
Foreign name: principal components analysis

Full Chinese name: Principal component analysis technology
Chinese nickname: Principal component analysis

catalog

one Product Introduction
two To transform

Product Introduction

Announce

edit

PCA (principal components analysis) principal component analysis Technology, also known as principal component analysis, aims to use Dimension reduction And transform multiple indicators into a few comprehensive indicators.

In statistics, principal component Analyzing PCA is a simplification data set Technology. It is a linear transformation 。 This transformation Data transformation To a new Coordinate system The first major variance of any data projection is in the first coordinate (called First principal component ）The second largest variance is on the second coordinate (the second principal component), and so on. principal component analysis Often used to reduce data sets dimension , while maintaining the feature that the dataset contributes the most to the variance. This is achieved by retaining lower order principal components and ignoring higher order principal components. In this way, low order components can often retain the most important aspects of data. However, this is not certain, but depends on the specific application.

To transform

Announce

edit

(1) The first step is to calculate the covariance matrix S (this is non-standard PCA, and standard PCA calculates correlation coefficient matrix C):

(2) Step 2 Calculation covariance matrix S (or C) feature vector e 1, e 2,…, e N and characteristic value , t = 1,2,…,N ；

(3) The third step is to project the data into the feature vector space. Use formula

Where BV value is the value of the corresponding dimension in the original sample.

PCA aims to find r (r ＜ n) new variables, make them reflect the main characteristics of things, compress the scale of the original data matrix feature vector The least dimension is selected to summarize the most important features. Each new variable is the original variable linear combination , reflecting the comprehensive effect of the original variables, with a certain practical meaning. These r new variables are called "principal components", which can reflect the influence of the original n variables to a large extent, and these new variables are uncorrelated and orthogonal. adopt principal component analysis , Compression Data space , will Multivariate data Is low Dimensional space It is intuitively shown in.

Novice on the road

Growth task Getting Started with Editing Edit Rule Edit by myself

I have questions

Content query Online Service Official post bar Feedback

Complaints and suggestions

Report bad information Failed to appeal through entries Complaint of infringement information Blocking query and unblocking

Jinggong Network Anbei No. 1100000200001