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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

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
Outline
  • Introduction
  • Related work
  • Problem definition
  • The learning algorithms
  • Experimental study
  • Summary and conclusions
introduction 1 3
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

introduction 2 3
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)
introduction 3 3
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.
problem definition
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.
the learning algorithms 1 2
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)
the learning algorithms 2 2
The learning algorithms(2/2)
  • SSSVaD vs. MPCK-Means
experimental study
Experimental study
  • Data sets(20 NewsGroup):
experimental study11
Experimental study
  • Effect of the Number of Clusters
  • (1)Binary
experimental study14
Experimental study
  • Effect of the Amount of Supervision
  • (1)Binary
experimental study17
Experimental study
  • Effect of Initialization
  • (1)Binary
summary and conclusions
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.