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A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery

2008

Conference Paper

ei


In recent work by (Song et al., 2007), it has been proposed to perform clustering by maximizing a Hilbert-Schmidt independence criterion with respect to a predefined cluster structure Y , by solving for the partition matrix, II. We extend this approach here to the case where the cluster structure Y is not fixed, but is a quantity to be optimized; and we use an independence criterion which has been shown to be more sensitive at small sample sizes (the Hilbert-Schmidt Normalized Information Criterion, or HSNIC, Fukumizu et al., 2008). We demonstrate the use of this framework in two scenarios. In the first, we adopt a cluster structure selection approach in which the HSNIC is used to select a structure from several candidates. In the second, we consider the case where we discover structure by directly optimizing Y.

Author(s): Blaschko, MB. and Gretton, A.
Book Title: MLG 2008
Journal: Proceedings of the 6th International Workshop on Mining and Learning with Graphs (MLG 2008)
Pages: 1-3
Year: 2008
Month: July
Day: 0

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 6th International Workshop on Mining and Learning with Graphs
Event Place: Helsinki, Finland

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{5179,
  title = {A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery},
  author = {Blaschko, MB. and Gretton, A.},
  journal = {Proceedings of the 6th International Workshop on Mining and Learning with Graphs (MLG 2008)},
  booktitle = {MLG 2008},
  pages = {1-3},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2008},
  month_numeric = {7}
}