The choice of how we reduce dimensionality can really change how we understand our data. Here’s my take on it:
Keeping the Structure: PCA, which stands for Principal Component Analysis, does a good job of maintaining the big picture of the data. It works well for data that’s organized in a straight line. But t-SNE and UMAP focus more on the small details. They do a better job of showing groups or clusters within the data.
Understanding the Results: With PCA, it’s easier to understand the results because it uses combinations of the original data features. On the other hand, t-SNE is a bit like a mystery box. It’s harder to figure out what’s going on inside.
Speed of Calculation: PCA is quick, especially when dealing with large amounts of data. t-SNE can take longer but often gives richer and more detailed pictures of the data.
In the end, the method you pick can lead to very different insights, or understandings, of your data!
The choice of how we reduce dimensionality can really change how we understand our data. Here’s my take on it:
Keeping the Structure: PCA, which stands for Principal Component Analysis, does a good job of maintaining the big picture of the data. It works well for data that’s organized in a straight line. But t-SNE and UMAP focus more on the small details. They do a better job of showing groups or clusters within the data.
Understanding the Results: With PCA, it’s easier to understand the results because it uses combinations of the original data features. On the other hand, t-SNE is a bit like a mystery box. It’s harder to figure out what’s going on inside.
Speed of Calculation: PCA is quick, especially when dealing with large amounts of data. t-SNE can take longer but often gives richer and more detailed pictures of the data.
In the end, the method you pick can lead to very different insights, or understandings, of your data!