What is Principal Component Analysis ?

What is Dimensionality Reduction ?

Dimension reduction is the process of reducing the number of dimensions and reducing the variable available by considering a few essential features. It can also defined as the technique that converts the large-dimension dataset to the small-dimension data set by considering the essential features. The dimensions reduction technique is mainly used when we are dealing with a large amount of data set. The few methods in dimension reduction are principal component analysis, wavelet transforms, singular value decomposition, linear discriminant analysis, generalized discriminant analysis, and many more....

What is Principal Component Analysis ?

Principal component analysis is a useful and an important method for the dimensionality reduction in the data pre processing . Principal Component analysis is served as a unsupervised dimensionality reduction technique . In the principal component analysis we almost consider the variance of the data points , without considering the other class labels . We reduce the data based on the variance of the data points without considering the other class labels or dependent variable in the provided information ....

Steps in Principal Component Analysis

These are the few steps in principal component analysis...

Example of Principal Component Analysis

In this section , we discuss an example how to solve the principal component analysis mathematically....

Implementation of the principle component analysis using R

Step-1 : Loading the input data...

Conclusion

In conclusion , principal component analysis plays a major role in the dimensionality reduction . In this article we have learned the basic concepts of the principal component analysis , its implementation in r programming , its applications in different fields , advantages and disadvantages....