In unsupervised learning, an input set is provided to the system during the training phase. In contrast to supervised learning, the input objects are not labeled with their class. Although in classification analysis the training dataset is labeled, we do not always have that advantage when we collect data in the real world, but still we want to find important values or hidden structures of the data. In NeuralIPS' 2016, Facebook AI Chief Yann LeCun introduced the cake analogy:
"If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We know how to make the icing and the cherry, but we don't know how to make the cake."
In order to create such a cake, several unsupervised learning tasks, including clustering...
