Replacing missing values with mean of feature calculated from previously replaced values
So, is it alright to replace missing values with the mean calculated from values that include some that have once replaced other missing values?


There's no law against it :)

But by replacing missing values everywhere in many different ways you're probably damaging the reliability of your dataset at some point, and that could in turn mess up your experiment.

You don't give any detail about the task or the data so I assume you chose this approach on purpose, but just in case let me remind you of other options:


you could discard features for which most values are missing, unless they happen to be very important for the task
you could discard instances which have many/some missing values, especially if you have a very large dataset anyway
you could use a ML method able to deal with missing values by itself