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Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering |
Greene, Derek; Cunningham, Padraig
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TCD-CS-2006-04 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 discuss a selection of strategies for addressing
this issue, and evaluate their effectiveness in producing more accurate
and stable clusterings.
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Keyword(s):
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Computer Science |
Publication Date:
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2006 |
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Type:
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Report |
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Peer-Reviewed:
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Unknown |
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Language(s):
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English |
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Institution:
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Trinity College Dublin |
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Citation(s):
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Greene, Derek; Cunningham, Padraig. 'Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2006-04, 2006, pp14 |
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Publisher(s):
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Trinity College Dublin, Department of Computer Science |
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File Format(s):
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application/pdf |
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First Indexed:
2014-05-13 05:30:50 Last Updated:
2015-04-10 05:13:42 |
