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Overview:
This paper revisits recently proposed algorithms for probabilistic clustering with pair-wise constraints between data points. Existing techniques are evaluated and compared in terms of robustness to misspecified constraints. It is shown that the technique that strictly enforces the given constraints, namely the chunklet model, produces poor results even under a small number of misspecified constraints. The paper further shows that methods that penalize constraint violation are more robust to misspecified constraints but have undesirable local behaviors. Based on this evaluation, a new learning technique is proposed, extending the chunklet model to allow soft constraints represented by an intuitive measure of confidence in the constraint.
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| Format: | Size: | 268 KB | |
| Date: | Jun 2007 | ||
| Pages: | 9 |
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