One Hot encoding for large number of values How do we use one hot encoding if the number of values which a categorical variable can take is large ?

In my case it is 56 values. So as per usual method I would have to add 56 columns (56 binary features) in the training dataset which will immensely increase the complexity and hence the training time.

So how do we deal with such cases ?
If you really care about the number of dimensions, you still can try to apply a dimensionality reduction algorithm, such as PCA (Principal Component Analysis) or LDA (Linear Discriminant Analysis), after your one hot encoding. 

But know that "56 features" isn't really large and it's highly common in the industry to have thousands, millions or even billions of features.