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TNN
2010

Incorporating the loss function into discriminative clustering of structured outputs

8 years 8 months ago
Incorporating the loss function into discriminative clustering of structured outputs
Clustering using the Hilbert Schmidt independence criterion (CLUHSIC) is a recent clustering algorithm that maximizes the dependence between cluster labels and data observations according to the Hilbert Schmidt independence criterion (HSIC). It is unique in that structure information on the cluster outputs can be easily utilized in the clustering process. However, while the choice of the loss function is known to be very important in supervised learning with structured outputs, we will show in this paper that CLUHSIC is implicitly using the often inappropriate zero-one loss. We propose an extension called CLUHSICAL (which stands for "Clustering using HSIC and loss") which explicitly considers both the output dependency and loss function. Its optimization problem has the same form as CLUHSIC, except that its partition matrix is constructed in a different manner. Experimental results on a number of datasets with structured outputs show that CLUHSICAL often outperforms CLUHSIC i...
Wenliang Zhong, Weike Pan, James T. Kwok, Ivor W.
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TNN
Authors Wenliang Zhong, Weike Pan, James T. Kwok, Ivor W. Tsang
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