When I learn dimension reduction technique I found that PCA and t-SNE are widely used as defined below:
Principal Component Analysis: This is one of the most widely used techniques for dealing with linear data. It divides the data into a set of components which try to explain as much variance as possible
t-SNE: This technique also works well when the data is strongly non-linear. It works extremely well for visualizations as well
The problem is that I don't know what is the meaning of linear and non-linear data ? Could you explain in detail ? Many Thank!