<p>The following publication shows great and meaningful results when computing PCA on categorical variables treated as simplex vertices:</p>

<blockquote>
  <p>Niitsuma H., Okada T. (2005) Covariance and PCA for Categorical Variables. In: Ho T.B., Cheung D., Liu H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science, vol 3518. Springer, Berlin, Heidelberg</p>
  
  <p><a href="https://doi.org/10.1007/11430919_61" rel="nofollow noreferrer">https://doi.org/10.1007/11430919_61</a></p>
</blockquote>

<p>It is available via <a href="https://arxiv.org/abs/0711.4452" rel="nofollow noreferrer">https://arxiv.org/abs/0711.4452</a> (including as a PDF).</p>
PCA For categorical features?
<p>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:</p>

<p><a href="https://datascience.stackexchange.com/questions/9443/when-to-use-one-hot-encoding-vs-labelencoder-vs-dictvectorizor">When to use One Hot Encoding vs LabelEncoder vs DictVectorizor?</a></p>

<p>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.</p>