Principal component analysis is a multivariate statistical method that examines the correlation between multiple variables. It studies how to reveal the internal structure of multiple variables through a few principal components, that is, to derive a few principal components from the original variables and make them Keep as much information of the original variables as possible, and they are not related to each other. Usually the mathematical processing is to make a linear combination of the original P indicators as a new comprehensive indicator.
Figure 1. Principal component analysis PCA of a multivariate Gaussian distribution
When using statistical analysis methods to study multivariate topics, too many variables will increase the complexity of the topic. People naturally hope that there are fewer variables and more information. In many cases, there is a certain correlation between variables. When there is a certain correlation between two variables, it can be explained that the two variables reflect the information of this subject to a certain degree of overlap. Principal component analysis is to delete the redundant variables (closely related variables) for all the variables originally proposed, and establish as few new variables as possible, so that these new variables are pairwise unrelated, and these new variables are reflecting Keep the original information as much as possible in the information aspect of the subject. Try to recombine the original variables into a new set of several unrelated comprehensive variables, and at the same time, according to actual needs, a few fewer comprehensive variables can be taken out of them to reflect as much information of the original variables as possible. The statistical method is called principal component analysis or so called. Principal component analysis is also a method used in mathematics to reduce dimensionality.
The principal component analysis provided by MedAI can reduce the dimensionality of the data space under study.
MedAI provides you with the principal component analysis service. By drawing the conclusion of factor load aij, you can clarify some relationships between X variables.
Graphic representation of our multidimensional data, and regression models can be constructed.
The systems we can analyze include but not limited to：
|Project name||Principal component analysis|
|Cycle||Depends on the time you need to simulate and the time required for the system to reach equilibrium.|
|Product delivery mode||The simulation results provide you with the raw data and analysis results of molecular dynamics.|
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