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Integrating Constraints and Metric Learning in Semi-Supervised Clustering

Integrating Constraints and Metric Learning in Semi-Supervised Clustering. Mikhail Bilenko, Sugato Basu, Raymond J. Mooney ICML 2004 Presented by Xin Li. Semi-Supervised Clustering. K=4. Semi-Supervised Clustering. Semi-Supervised Clustering. How to exploit supervision in clustering.

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Integrating Constraints and Metric Learning in Semi-Supervised Clustering

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  1. Integrating Constraints and Metric Learning in Semi-Supervised Clustering Mikhail Bilenko, Sugato Basu, Raymond J. Mooney ICML 2004 Presented by Xin Li

  2. Semi-Supervised Clustering K=4

  3. Semi-Supervised Clustering

  4. Semi-Supervised Clustering

  5. How to exploit supervision in clustering • Incorporate supervision as constraints • Learn a distance metric using supervision • Integration of these two approaches

  6. K-means Clustering X = {x1,x2,…} L = {l1,l2,…,lk} Euclidean Distance: Minimizing:

  7. Clustering with constraints Pairwise constraints: • M – Must-link pairs • (xi, xj) should be in the same cluster • C -- Cannot-link pairs • (xi, xj) should be in different clusters

  8. Learning a pairwise distance metric Binary Classification: (xi, xj)  0/1 • M  positive examples • (xi, xj) are the same cluster • C  negative examples • (xi, xj) are in different clusters • Apply the learned distance metric in clustering • Metric learning and clustering are disjointed

  9. Maximizing the complete data log-likelihood under generalized K-means Unsupervised Clustering with Metric Learning Learn a distance metric that optimize a quality function

  10. Integrating Constraints and Metric Learning Combining the previous two equations leads to the following objective function that minimizes cluster dispersion under that learned metrics while reducing constraint violations.

  11. Penalty for violating constraints • Penalty for violating a must-link constraints between distant points should be higher than that between nearby points. • Penalty for violating a cannot-link constraints between nearby points should be lower than that between nearby points.

  12. MPCK-MEANS Algorithm • Constraints are utilized during cluster initialization and when assigning points to clusters. • The distance metric is adapted by re-estimating the weights in matrices Ah.

  13. Initialization • An initial guess of the clusters. • Assign each point x to one of K clusters in a way that satisfies the constraints. • Compute the centroid of each cluster.

  14. E-step • Every point x is assigned to the cluster that minimizes the sum of the distance of x to the cluster centroid according to the local metric and the cost of any constraint violations incurred by the cluster assignment.

  15. = 0 Update Metrics: M-Step

  16. Experimental Setting

  17. Single Metric, Diagonal Matrix A

  18. Single Metric, Diagonal Matrix A

  19. Multiple Metrics, Full Matrix A

  20. Multiple Metrics, Full Matrix A

  21. Conclusion and Discussion • This paper has presented MPCK-MEANS, a new approach to semi-supervised clustering. • Supervision and metric learning are helpful in clustering and multiple distance metrics are not necessary in most cases. • Question 1: If we have supervision in clustering, why not utilize supervision in the same way as in a typical classification task ? • Question 2: If there are infinite number of classes, can we gain from supervision on part of them ?

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