Hi Yimin,
When performing Principal Component Analysis (PCA) using MATLAB's `pca` function, it's important to consider the scaling of your data, as it can significantly affect the results. Here's a breakdown of what you need to know:
Centering vs. Scaling
1. Centering:
- By default, the `pca` function in MATLAB centers the data by subtracting the mean of each variable. This step is crucial as it ensures that the first principal component describes the direction of maximum variance.
2. Scaling:
- Scaling involves dividing each variable by its standard deviation so that each variable contributes equally to the analysis.
- Whether you need to scale your data depends on the nature of your data and the relative importance of the variables.
When to Scale
- Different Units or Scales: If your variables are measured in different units or have vastly different scales, scaling is generally recommended. This ensures that no single variable dominates the PCA results due to its larger magnitude.
- Equal Importance: If you believe all variables should contribute equally to the PCA, scaling is appropriate.
- Natural Scales: If your variables are already on a similar scale or if the differences in scale are meaningful (e.g., when the magnitude of variables reflects their importance), you might choose not to scale.
Hope this helps.
