Unsupervised algorithms are not unique to neural networks, as K-means, expectation maximization, and methods of moments are also examples of unsupervised learning algorithms. One common feature of all learning algorithms is the absence of mapping among variables in the current dataset; instead, one wishes to find a different meaning of this data, and that's the goal of any unsupervised learning algorithm.
While in supervised learning algorithms, we usually have a smaller number of outputs, for unsupervised learning, there is a need to produce an abstract data representation that may require a high number of outputs, but, except for classification tasks, their meaning is totally different than the one presented in the supervised learning. Usually, each output neuron is responsible for representing a feature or a class present in the input data. In most architectures, not all output neurons need to be activated at a time; only a restricted set of output neurons...