Active learning of constraints for semi-supervised clustering Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/k3569745g

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • Semi-supervised clustering aims to improve clustering performance by considering user supervision in the form of pairwise constraints. In this paper, we study the active learning problem of selecting pairwise must-link and cannot-link constraints for semisupervised clustering. We consider active learning in an iterative manner where in each iteration queries are selected based on the current clustering solution and the existing constraint set. We apply a general framework that builds on the concept of neighborhood, where neighborhoods contain "labeled examples" of different clusters according to the pairwise constraints. Our active learning method expands the neighborhoods by selecting informative points and querying their relationship with the neighborhoods. Under this framework, we build on the classic uncertainty-based principle and present a novel approach for computing the uncertainty associated with each data point. We further introduce a selection criterion that trades-off the amount of uncertainty of each data point with the expected number of queries (the cost) required to resolve this uncertainty. This allows us to select queries that have the highest information rate. We evaluate the proposed method on the benchmark datasets and the results demonstrate consistent and substantial improvements over the current state-of-the-art.
Resource Type
Date Available
Date Copyright
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Non-Academic Affiliation
Keyword
Subject
Rights Statement
Peer Reviewed
Language
Replaces
Additional Information
  • description.provenance : Made available in DSpace on 2013-05-09T17:40:53Z (GMT). No. of bitstreams: 1 XiongSicheng2013.pdf: 1039551 bytes, checksum: ca171ad70728c612cfa253ddf69adc2f (MD5) Previous issue date: 2013-04-25
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2013-05-09T17:40:53Z (GMT) No. of bitstreams: 1 XiongSicheng2013.pdf: 1039551 bytes, checksum: ca171ad70728c612cfa253ddf69adc2f (MD5)
  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2013-05-09T15:31:03Z (GMT) No. of bitstreams: 1 XiongSicheng2013.pdf: 1039551 bytes, checksum: ca171ad70728c612cfa253ddf69adc2f (MD5)
  • description.provenance : Submitted by Sicheng Xiong (xiongs@onid.orst.edu) on 2013-05-09T00:07:44Z No. of bitstreams: 1 XiongSicheng2013.pdf: 1039551 bytes, checksum: ca171ad70728c612cfa253ddf69adc2f (MD5)

Relationships

Parents:

This work has no parents.

Last modified

Downloadable Content

Download PDF

Items