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Practical solutions to the problem of diagonal dominance in kernel document clustering

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Practical solutions to the problem of diagonal dominance in kernel document clustering
In supervised kernel methods, it has been observed that the performance of the SVM classifier is poor in cases where the diagonal entries of the Gram matrix are large relative to the off-diagonal entries. This problem, referred to as diagonal dominance, often occurs when certain kernel functions are applied to sparse high-dimensional data, such as text corpora. In this paper we investigate the implications of diagonal dominance for unsupervised kernel methods, specifically in the task of document clustering. We propose a selection of strategies for addressing this issue, and evaluate their effectiveness in producing more accurate and stable clusterings.
Derek Greene, Padraig Cunningham
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2006
Where ICML
Authors Derek Greene, Padraig Cunningham
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