Please use this identifier to cite or link to this item:
Title: Multi-view positive and unlabeled learning
Authors: Zhou, Joey Tianyi
Pan, Sinno Jialin
Mao, Qi
Tsang, Ivor W.
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Zhou, J. T., Pan, S. J., Mao, Q., & Tsang, I. W. (2012). Multi-view positive and unlabeled learning. Journal of machine learning research: workshop and conference proceedings, 25, 555-570.
Abstract: Learning with Positive and Unlabeled instances (PU learning) arises widely in information retrieval applications. To address the unavailability issue of negative instances, most existing PU learning approaches require to either identify a reliable set of negative instances from the unlabeled data or estimate probability densities as an intermediate step. However, inaccurate negative-instance identication or poor density estimation may severely degrade overall performance of the final predictive model. To this end, we propose a novel PU learning method based on density ratio estimation without constructing any sets of negative instances or estimating any intermediate densities. To further boost PU learning performance, we extend our proposed learning method in a multi-view manner by utilizing multiple heterogeneous sources. Extensive experimental studies demonstrate the effectiveness of our proposed methods, especially when positive labeled data are limited.
Rights: © 2012 The Authors(Journal of Machine Learning Research). This paper was published in Journal of Machine Learning Research and is made available as an electronic reprint (preprint) with permission of The Authors(Journal of Machine Learning Research). The paper can be found at the following official URL: [].  One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

Files in This Item:
File Description SizeFormat 
Multi-view Positive and Unlabeled Learning.pdf764.65 kBAdobe PDFThumbnail

Page view(s) 10

Updated on Feb 7, 2023

Download(s) 5

Updated on Feb 7, 2023

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.