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Kernel-based hard clustering methods in the feature space with automatic variable weighting
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DOI：10.1016/j.patcog.2014.03.026.png)
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This paper presents variable-wise kernel hard clustering algorithms in the feature space in which dissimilarity measures are obtained as sums of squared distances between patterns and centroids computed individually for each variable by means of kernels. The methods proposed in this paper are supported by the fact that a kernel function can be written as a sum of kernel functions evaluated on each variable separately. The main advantage of this approach is that it allows the use of adaptive distances, which are suitable to learn the weights of the variables on each cluster, providing a better performance. Moreover, various partition and cluster interpretation tools are introduced. Experiments with synthetic and benchmark datasets show the usefulness of the proposed algorithms and the merit of the partition and cluster interpretation tools. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
Kernel clustering
Feature space
Adaptive distances
Clustering analysis
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7.6
论文数:
1.3W
被引数:
4.5W
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MACHINE LEARNINGIF2.9
OAAI分享收藏Evaluation of the performance of clustering algorithms in kernel-induced feature spacePATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
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