Semisupervised clustering with metric learning using relative comparisons
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Semisupervised Clustering with Metric Learning Using Relative Comparisons. Nimit Kumar, Member, IEEE, and Krishna Kummamuru IEEE Transactions On Knowledge And Data Engineering Volume:20, Issue:4, Pages:496-503 指導老師:陳彥良 教授 、許秉瑜 教授 報 告 人:林欣瑾. 中華民國 97 年 8 月 14 日.

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Semisupervised clustering with metric learning using relative comparisons l.jpg

Semisupervised Clustering with Metric LearningUsing Relative Comparisons

Nimit Kumar, Member, IEEE, and Krishna Kummamuru

IEEE Transactions On Knowledge And Data Engineering

Volume:20, Issue:4, Pages:496-503

指導老師:陳彥良教授、許秉瑜教授

報 告 人:林欣瑾

中華民國97年8月14日


Outline l.jpg
Outline

  • Introduction

  • Related work

  • Problem definition

  • The learning algorithms

  • Experimental study

  • Summary and conclusions


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Introduction(1/3)

  • Semisupervised clustering algorithms are becoming more popular mainly because of

    (1)the abundance of unlabeled data

    (2)the high cost of obtaining labeled data

  • The most popular form of supervision used in clustering algorithms is in terms of pairwise feedback

    →must-links: data points belonging to the same cluster

    →cannot-link: data points belonging to the different cluster


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Introduction(2/3)

  • The pairwise constraints have two drawbacks:

    (1) The points in cannot-link constraints may actually lie in wrong clusters and still satisfy the cannot-link constraints (2) the must-link constraint would mislead the clustering algorithm if the points in the constraint belong to two different clusters of the same class.

  • Supervision to be available in terms of relative comparisons: x is close to y than to z. (as triplet constraints)


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Introduction(3/3)

  • This paper call the proposed algorithm Semisupervised SVaD (SSSVaD)

  • Assume a set of labeled data, relative comparisons can be obtained from any three points from the set if two of them belong to a class different from the class of the third point.

  • Triplet constraints give more information on the underlying dissimilarity measure than the pairwise constraints.



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Problem definition

  • Given a set of unlabeled samples and triplet constraints, the objective of SSSVaD is to find a partition of the data set along with the parameters of the SVaD measure that minimize the within-cluster dissimilarity while satisfying as many triplet constraints as possible.


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The learning algorithms(1/2)

  • 1.Spatially Variant Dissimilarity (SVaD)

  • 2.Semisupervised SVaD (SSSVaD)

  • 3.Metric pairwise constrained K-Means

    (MPCK-Means)

  • 4.rMPCK-Means

  • 5.K-Means Algorithms (KMA)


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The learning algorithms(2/2)

  • SSSVaD vs. MPCK-Means


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Experimental study

  • Data sets(20 NewsGroup):


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Experimental study

  • Effect of the Number of Clusters

  • (1)Binary



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Experimental study

  • (3)Multi10


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Experimental study

  • Effect of the Amount of Supervision

  • (1)Binary



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Experimental study

  • (3)Multi10


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Experimental study

  • Effect of Initialization

  • (1)Binary


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Experimental study

  • (2)Multi10


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Summary and conclusions

  • The efficiency of relative comparisons over pairwise constraints was established through exhaustive experimentations.

  • The proposed algorithm (SSSVaD) achieves higher accuracy and is more robust than similar algorithms using pairwise constraints for supervision.



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