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Sunday, March 4, 2012

Difference between Self-taught learning and semi-supervised learning settings

There are 2 common type of unsupervised learning settings:
  • Self-taught learning
  • Semi-supervised learning
   Self-taught learning setting is more versatile, broadly applicable and does not assume that your unlabeled data has to be drawn from the same distribution as your labeled data. In Self-taught learning setting it is not necessary that most of the unlabelled data belongs to at least one class, it may happen that appreciable amount of data does not belong to any class. 

   Semi-supervised learning setting assumes that unlabeled data comes from exactly the same distribution as the labeled data. In  Semi-supervised learning setting most of the unlabelled data belongs to one of the classes.

These two methods are most powerful in problems where we have a lot of unlabeled data, and a smaller amount of labeled data.

1 comment:

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