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Dimensionality reduction methods allow us to visualize the structure of large, highdimensional datasets by giving each datapoint a location in a twodimensional map. Sam Roweis was involved in the development of several different methods for producing maps that preserve local similarity by displaying very similar datapoints at nearby locations in the map without worrying too much about the map distances between dissimilar datapoints. One of these methods, called Stochastic Neighbor Embedding, converts the problem of finding a good map into the problem of matching two probability distributions. It uses the density under a highdimensional Gaussian centered at each datapoint to determine the probability of picking each of the other datapoints as a neighbor. It then uses exactly the same method to determine neighbor probabilities using the twodimensional locations of the corresponding map points. The aim is to move the map points so that the neighbor probabilities computed in the highdimensional dataspace are wellmodeled by the neighbor probabilities computed in the lowdimensional map. This leads to very nice maps for a variety of datasets. I will describe some further developments of this method that lead to even better maps.
Author Information
Geoffrey E Hinton (Google & University of Toronto)
Geoffrey Hinton received his PhD in Artificial Intelligence from Edinburgh in 1978 and spent five years as a faculty member at CarnegieMellon where he pioneered backpropagation, Boltzmann machines and distributed representations of words. In 1987 he became a fellow of the Canadian Institute for Advanced Research and moved to the University of Toronto. In 1998 he founded the Gatsby Computational Neuroscience Unit at University College London, returning to the University of Toronto in 2001. His group at the University of Toronto then used deep learning to change the way speech recognition and object recognition are done. He currently splits his time between the University of Toronto and Google. In 2010 he received the NSERC Herzberg Gold Medal, Canada's top award in Science and Engineering.
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