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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!

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