How to Perform a Kernel Principal Component Analysis using R #r #pca #kpca #kernel #nonlinear
This video is a step by step guide for performing a kernel principal component analysis (KPCA) using R.
Kernel PCA is a powerful dimensionality reduction technique that captures complex, nonlinear relationships in data, making it ideal for applications in machine learning and data analysis. By transforming data into a higher-dimensional feature space, kernel PCA enables the identification of patterns and structures that may not be evident in the original input space. In this method, the choice of kernel, such as the radial basis function (RBF) kernel, plays a crucial role in the effectiveness of the transformation. When performing kernel PCA, it’s important to select the right number of components based on explained variance, typically aiming for 80% or more to retain most of the data’s information. Visualization of the first few components can help identify clusters, outliers, and trends, offering valuable insights for further analysis.
The R codes used in this video are posted in the Comments.
[ad_2]
source
