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Spectral Clustering

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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.

This talk is part of the Machine Learning @ CUED series.

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