PCA For categorical features?
In my understanding, I thought PCA can be performed only for continuous features. But while trying to understand the difference between onehot encoding and label encoding came through a post in the following link:
When to use One Hot Encoding vs LabelEncoder vs DictVectorizor?
It states that one hot encoding followed by PCA is a very good method, which basically means PCA is applied for categorical features. Hence confused, please suggest me on the same.
Answer
I disagree with the others.
While you can use PCA on binary data (e.g. one-hot encoded data) that does not mean it is a good thing, or it will work very well.
PCA is desinged for continuous variables. It tries to minimize variance (=squared deviations). The concept of squared deviations breaks down when you have binary variables.
So yes, you can use PCA. And yes, you get an output. It even is a least-squared output - it's not as if PCA would segfault on such data. It works, but it is just much less meaningful than you'd want it to be; and supposedly less meaningful than e.g. frequent pattern mining.