A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery
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 |
| Bibtex Type: | Conference Paper (inproceedings) |
| Event Name: | 6th International Workshop on Mining and Learning with Graphs |
| Event Place: | Helsinki, Finland |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@inproceedings{5179,
title = {A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery},
journal = {Proceedings of the 6th International Workshop on Mining and Learning with Graphs (MLG 2008)},
booktitle = {MLG 2008},
abstract = {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.},
pages = {1-3},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
month = jul,
year = {2008},
slug = {5179},
author = {Blaschko, MB. and Gretton, A.},
month_numeric = {7}
}