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A Hilbert-Schmidt Dependence Maximization Approach to Unsupervised Structure Discovery
This video was recorded at 6th International Workshop on Mining and Learning with Graphs (MLG), Helsinki 2008. 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. 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.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
