research-article

Active learning for sparse bayesian multilabel classification

Authors:

Deepak Vasisht,

Andreas Damianou,

Ashish KapoorAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 472 - 481

https://doi.org/10.1145/2623330.2623759

Published: 24 August 2014 Publication History

Abstract

We study the problem of active learning for multilabel classification. We focus on the real-world scenario where the average number of positive (relevant) labels per data point is small leading to positive label sparsity. Carrying out mutual information based near-optimal active learning in this setting is a challenging task since the computational complexity involved is exponential in the total number of labels. We propose a novel inference algorithm for the sparse Bayesian multilabel model of [17]. The benefit of this alternate inference scheme is that it enables a natural approximation of the mutual information objective. We prove that the approximation leads to an identical solution to the exact optimization problem but at a fraction of the optimization cost. This allows us to carry out efficient, non-myopic, and near-optimal active learning for sparse multilabel classification. Extensive experiments reveal the effectiveness of the method.

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Cited By

Jha AAshwood ZPillow J(2024)Active Learning for Discrete Latent Variable ModelsNeural Computation10.1162/neco_a_0164636:3(437-474)Online publication date: 16-Feb-2024
https://doi.org/10.1162/neco_a_01646
Shrewsbury DKim SKim YKong HLee S(2024)Instance-Ambiguity Weighting for Multi-label Recognition with Limited AnnotationsAdvances in Knowledge Discovery and Data Mining10.1007/978-981-97-2242-6_13(156-167)Online publication date: 25-Apr-2024
https://doi.org/10.1007/978-981-97-2242-6_13
Abdelfattah RGuo QLi XWang XWang S(2023)CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.00130(1348-1357)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICCV51070.2023.00130
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Index Terms

Active learning for sparse bayesian multilabel classification
1. Computing methodologies
  1. Machine learning

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cover image ACM Conferences

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
Sofus Macskassy
Facebook
,
Claudia Perlich
Dstillery
,
Program Chairs:
Jure Leskovec
Stanford University
,
Wei Wang
UCLA
,
Rayid Ghani
University of Chicago

Copyright © 2014 ACM.

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Published: 24 August 2014

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

New York, New York, USA

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KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Jha AAshwood ZPillow J(2024)Active Learning for Discrete Latent Variable ModelsNeural Computation10.1162/neco_a_0164636:3(437-474)Online publication date: 16-Feb-2024
https://doi.org/10.1162/neco_a_01646
Shrewsbury DKim SKim YKong HLee S(2024)Instance-Ambiguity Weighting for Multi-label Recognition with Limited AnnotationsAdvances in Knowledge Discovery and Data Mining10.1007/978-981-97-2242-6_13(156-167)Online publication date: 25-Apr-2024
https://doi.org/10.1007/978-981-97-2242-6_13
Abdelfattah RGuo QLi XWang XWang S(2023)CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.00130(1348-1357)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICCV51070.2023.00130
Kim YKim JJeong JSchmid CAkata ZLee J(2023)Bridging the Gap Between Model Explanations in Partially Annotated Multi-Label Classification2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.00332(3408-3417)Online publication date: Jun-2023
https://doi.org/10.1109/CVPR52729.2023.00332
Xie XTian MLuo GLiu GWu YQin K(2023)Active learning in multi-label image classification with graph convolutional network embeddingFuture Generation Computer Systems10.1016/j.future.2023.05.028148(56-65)Online publication date: Nov-2023
https://doi.org/10.1016/j.future.2023.05.028
Wang ZLu JXiao HLiu SZhou J(2023)Learning Accurate Performance Predictors for Ultrafast Automated Model CompressionInternational Journal of Computer Vision10.1007/s11263-023-01783-0131:7(1761-1783)Online publication date: 13-Apr-2023
https://doi.org/10.1007/s11263-023-01783-0
Ji XTan AWu WGu S(2023)Multi-label classification with weak labels by learning label correlation and label regularizationApplied Intelligence10.1007/s10489-023-04562-z53:17(20110-20133)Online publication date: 30-Mar-2023
https://doi.org/10.1007/s10489-023-04562-z
Chong CYang XWang TKe WWang Y(2023)Category-Wise Fine-Tuning for Image Multi-label Classification with Partial LabelsNeural Information Processing10.1007/978-981-99-8145-8_26(332-345)Online publication date: 27-Nov-2023
https://doi.org/10.1007/978-981-99-8145-8_26
Gong XYuan DBao W(2022) Top- Partial Label Machine IEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.308339733:11(6775-6788)Online publication date: Nov-2022
https://doi.org/10.1109/TNNLS.2021.3083397
Gong XYang JYuan DBao W(2022)Generalized Large Margin $k$NN for Partial Label LearningIEEE Transactions on Multimedia10.1109/TMM.2021.310943824(1055-1066)Online publication date: 2022
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