research-article

Learning distance metric for regression by semidefinite programming with application to human age estimation

Authors:

Hongyuan ZhaAuthors Info & Claims

MM '09: Proceedings of the 17th ACM international conference on Multimedia

Pages 451 - 460

https://doi.org/10.1145/1631272.1631334

Published: 19 October 2009 Publication History

Abstract

A good distance metric for the input data is crucial in many pattern recognition and machine learning applications. Past studies have demonstrated that learning a metric from labeled samples can significantly improve the performance of classification and clustering algorithms. In this paper, we investigate the problem of learning a distance metric that measures the semantic similarity of input data for regression problems. The particular application we consider is human age estimation. Our guiding principle for learning the distance metric is to preserve the local neighborhoods based on a specially designed distance as well as to maximize the distances between data that are not in the same neighborhood in the semantic space.Without any assumption about the structure and the distribution of the input data, we show that this can be done by using semidefinite programming. Furthermore, the low-level feature space can be mapped to the high-level semantic space by a linear transformation with very low computational cost. Experimental results on the publicly available FG-NET database show that 1) the learned metric correctly discovers the semantic structure of the data even when the amount of training data is small and 2) significant improvement over the traditional Euclidean metric for regression can be obtained using the learned metric. Most importantly, simple regression methods such as k nearest neighbors (kNN), combined with our learned metric, become quite competitive (and sometimes even superior) in terms of accuracy when compared with the state-of-the-art human age estimation approaches.

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This supplement contains corrections to Tables 1 and 2 and to Figure 8 in the article "Learning distance metric for regression by semidefinite programming with application to human age estimation."

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Cited By

Shao WLiu JZuo YQi SHong HSheng JZhu QZhang D(2023)FAM3L: Feature-Aware Multi-Modal Metric Learning for Integrative Survival Analysis of Human CancersIEEE Transactions on Medical Imaging10.1109/TMI.2023.326202442:9(2552-2565)Online publication date: Sep-2023
https://doi.org/10.1109/TMI.2023.3262024
Li WLu JWuerkaixi AFeng JZhou J(2022)MetaAge: Meta-Learning Personalized Age EstimatorsIEEE Transactions on Image Processing10.1109/TIP.2022.318806131(4761-4775)Online publication date: 2022
https://doi.org/10.1109/TIP.2022.3188061
Qin XLiu DWang DGui B(2022)Understanding More Types of Social Relationships Using Clothing and Distance Metric LearningNeural Processing Letters10.1007/s11063-022-10842-554:6(4951-4970)Online publication date: 8-May-2022
https://doi.org/10.1007/s11063-022-10842-5
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Index Terms

Learning distance metric for regression by semidefinite programming with application to human age estimation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
  2. Machine learning
2. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

MM '09: Proceedings of the 17th ACM international conference on Multimedia

October 2009

1202 pages

ISBN:9781605586083

DOI:10.1145/1631272

General Chairs:
Wen Gao
Peking University, China
,
Yong Rui
Microsoft, China
,
Alan Hanjalic
Delft University of Technology, The Netherlands
,
Program Chairs:
Changsheng Xu
Institute of Automation, Chinese Academy of Sciences, China
,
Eckehard Steinbach
Technical University of Munich, Germany
,
Abdulmotaleb El Saddik
University of Ottawa, Canada
,
Michelle Zhou
IBM T. J. Watson Research Center, USA

Copyright © 2009 ACM.

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Published: 19 October 2009

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MM09: ACM Multimedia Conference

October 19 - 24, 2009

Beijing, China

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Cited By

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https://doi.org/10.1109/TMI.2023.3262024
Li WLu JWuerkaixi AFeng JZhou J(2022)MetaAge: Meta-Learning Personalized Age EstimatorsIEEE Transactions on Image Processing10.1109/TIP.2022.318806131(4761-4775)Online publication date: 2022
https://doi.org/10.1109/TIP.2022.3188061
Qin XLiu DWang DGui B(2022)Understanding More Types of Social Relationships Using Clothing and Distance Metric LearningNeural Processing Letters10.1007/s11063-022-10842-554:6(4951-4970)Online publication date: 8-May-2022
https://doi.org/10.1007/s11063-022-10842-5
Kumar SSingh SKumar JPrasad K(2022)Age and gender classification using Seg-Net based architecture and machine learningMultimedia Tools and Applications10.1007/s11042-021-11499-381:29(42285-42308)Online publication date: 26-Jul-2022
https://doi.org/10.1007/s11042-021-11499-3
Sawant MBhurchandi K(2022)Discriminative aging subspace learning for age estimationSoft Computing10.1007/s00500-022-07333-z26:18(9189-9198)Online publication date: 14-Jul-2022
https://doi.org/10.1007/s00500-022-07333-z
Duan MOuyang ATan GTian Q(2021)Age Estimation Using Aging/Rejuvenation Features With Device-Edge SynergyIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2020.298111731:2(608-620)Online publication date: Feb-2021
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Suárez JGarcía SHerrera F(2021)Ordinal regression with explainable distance metric learning based on ordered sequencesMachine Language10.1007/s10994-021-06010-w110:10(2729-2762)Online publication date: 7-Jul-2021
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Duan YLiu MDong M(2020)A Metric-Learning-Based Nonlinear Modeling Algorithm and Its Application in Key-Performance-Indicator PredictionIEEE Transactions on Industrial Electronics10.1109/TIE.2019.293597967:8(7073-7082)Online publication date: Aug-2020
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Tang MPérez-Fernández RDe Baets B(2020)Distance metric learning for augmenting the method of nearest neighbors for ordinal classification with absolute and relative informationInformation Fusion10.1016/j.inffus.2020.08.004Online publication date: Aug-2020
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Kalintha WOno SNumao MFukui K(2019)Kernelized evolutionary distance metric learning for semi-supervised clusteringIntelligent Data Analysis10.3233/IDA-18428323:6(1271-1297)Online publication date: 10-Nov-2019
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