Consider a system which receives some sequence of inputs x1, x2, x3, …, where xt is the sensory input at time t. This input, called the data, could correspond to an image on the retina, the pixels in a camera, or a sound waveform. It could also correspond to less-obviously sensory data, for example, the words in a news story, or the list of items in a supermarket shopping basket. In
unsupervised learning, the system simply receives inputs x1, x2, …, but obtains neither supervised target outputs, nor rewards from its environment. It may seem somewhat mysterious to imagine what the system could possibly learn, given that it does not get any feedback from its environment. However, it is possible to develop a formal framework for
unsupervised learning based on the notion that the system’s goal is to build representations of the input that can be used for decision-making, predicting future inputs, efficiently communicating the inputs to another system, and so forth. In a sense,
unsupervised learning can be thought of as finding patterns in the data above and beyond what would be considered pure, unstructured noise. Two very simple classic examples of
unsupervised learning are clustering and dimensionality reduction.
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