I have a dataset with 1228 peaks and 15 features, and I am trying to use PCA analysis to reduce dimensionality and discover the most useful features describing the dataset.
When I ran PCA analysis using R's prcomp, I found that no principle component explains the majority of the variance in my dataset.
The second plot shows that the first 9 PCs together explain 90% of the variance in the dataset.
What I can see is that this means that PCA is probably useless for analysis of this dataset. But can I make any other observations about the nature of this data? What does it mean if none of the PCs explain the majority of the variance in the dataset?