Semisupervised Clustering with Metric Learning using Relative Comparisons

1. Semisupervised Clustering with Metric Learning using Relative Comparisons Author: Nimit Kumar, Member, IEEE and Krishna KUmmamuru Reporter: Wen-Cheng Tsai 2008/04/15 TKDE , 2008

2. Outline • Motivation • Objective • Introduction • Spatially Variant Dissimilarity Measure • Method • SSSVaD (Semisupervised Spatially variant dissimilarity) • Experiments • Conclusions • Comments

3. Motivation • The pairwise constraints have two drawbacks. • The points in cannot-link constraints may actually lie in wrong clusters and still satisfy the cannot-link constraints. • When the pairwise feedback is generated from the labeled part of the training set, the must-link constraints would mislead the clustering algorithm if the points in the constraint belong to two different clusters of the same class. pairwise Relative comparison

4. Objective Use the different dissimilarity measure matrix Relative comparisons 傳統：Pairwise constraints We demonstrate that the proposed algorithm achieves higher accuracy and is more robust than similar algorithms using pairwise constraints for supervision.

5. Spatially Variant Dissimilarity Measure y 目標函數 群內變異 Relative = + x Relative Comparisons x1 Pairwise x3 x2 =

6. SSVaD Learning Algorithm 目標函數 群內變異 Relative = +

7. Experiments

8. Experiments

9. Conclusions • SSSVaD algorithm uses a generalized dissimilarity measure(SVaD) and supervision in the form of relative comparisons. • SVaD helped to identify compact paritions in the data set by learning the inherent metric. • The relative comparisons proved to be highly efficient.

10. Comments • Advantages • … • Disadvantages • A few of examples • Application • Clustering

11. Appendix：MPCK-Means