<p>In <a href="https://arxiv.org/ftp/arxiv/papers/2111/2111.14839.pdf" rel="nofollow noreferrer">this paper</a>, the author's use PCA to combine categorical features of high cardinality. If I understood correctly, they first calculate conditional probabilities for each target class. Then they choose a threshold hyperparameter and create a new binary variable for each conditional class probability for each categorical feature to be combined. PCA is performed to combine the new binary variables with the number of components retained specified as a hyperparameter.</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>