University of Washington - Department of Statistics
Spectral clustering is a technique for finding groups in data consisting of similarities between pairs of points. We approach the problem of learning the similarity as a function of other (possibly diadic) observed features, in order to optimize spectral clustering results on future data. This paper formulates a new objective for learning in spectral clustering, that balances a clustering accuracy term (the gap), and a stability term (the eigengap) with the later in the role of a regularizer. We derive an algorithm to optimize this objective, and semiautomatic methods to choose the optimal regularization. Preliminary experiments confirm the validity of the approach.
Joint work with Susan Shortreed and Liang Xu.