Spectral Clustering

author: Arik Azran, Department of Engineering, University of Cambridge
published: March 3, 2008,   recorded: October 2007,   views: 7474
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Description

Machine Learning Tutorial Lecture Spectral clustering is a technique for finding group structure in data. It is based on viewing the data points as nodes of a connected graph and clusters are found by partitioning this graph, based on its spectral decomposition, into subgraphs that posses some desirable properties. My plan for this talk is to give a review of the main spectral clustering algorithms, demonstrate their abilities and limitations and offer some insight into when the method can be expected to be successful. No previous knowledge is assumed, and anyone who is interested in clustering (or fun applications of linear algebra) might find this talk interesting.

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Reviews and comments:

Comment1 Arjun Jain, January 25, 2009 at 11:13 p.m.:

why is it not full? where is the rest of the lecture?


Comment2 Daniel, January 13, 2014 at 3:20 p.m.:

Very accessible tutorial but the video stops abruptly in the middle of the lecture.

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