Graph methods and geometry of data

author: Mikhail Belkin, Department of Computer Science and Engineering, Ohio State University
published: Sept. 7, 2007,   recorded: September 2007,   views: 9447

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In recent years graph-based methods have seen success in different machine learning applications, including clustering, dimensionality reduction and semi-supervised learning. In these methods a graph is associated to a data set, after which certain aspects of the graph are used for various machine learning tasks. It is, however, important to observe that such graphs are empirical objects corresponding to a randomly chosen set of data points. In my talk I will discuss some of our work on using spectral graph methods for dimensionality reduction and semi-supervised learning and certain theoretical aspects of these methods, in particular, when data is sampled from a low-dimensional manifold.

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