What are the assumptions taken into consideration while applying PCA?

Data Science, AI and ML
#1

The assumptions needed for PCA are as follows:

1. PCA is based on Pearson correlation coefficients. As a result, there needs to be a linear relationship between the variables for applying the PCA algorithm.

2. For getting reliable results by using the PCA algorithm, we require a large enough sample size i.e, we should have sampling adequacy.

3. Your data should be suitable for data reduction i.e., we need to have adequate correlations between the variables to be reduced to a smaller number of components.

4. No significant noisy data or outliers are present in the dataset.