Semisupervised clustering with metric learning using relative comparisons l.jpg
This presentation is the property of its rightful owner.
Sponsored Links
1 / 20

Semisupervised Clustering with Metric Learning Using Relative Comparisons PowerPoint PPT Presentation


  • 262 Views
  • Updated On :
  • Presentation posted in: Sports / Games

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.

Download Presentation

Semisupervised Clustering with Metric Learning Using Relative Comparisons

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


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

  • Introduction

  • Related work

  • Problem definition

  • The learning algorithms

  • Experimental study

  • Summary and conclusions


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)

  • 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)

  • 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.


Related work


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)

  • 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)

  • SSSVaD vs. MPCK-Means


Experimental study

  • Data sets(20 NewsGroup):


Experimental study

  • Effect of the Number of Clusters

  • (1)Binary


Experimental study

  • (2)Multi5


Experimental study

  • (3)Multi10


Experimental study

  • Effect of the Amount of Supervision

  • (1)Binary


Experimental study

  • (2)Multi5


Experimental study

  • (3)Multi10


Experimental study

  • Effect of Initialization

  • (1)Binary


Experimental study

  • (2)Multi10


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.


Thanks for your listening


  • Login