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Heterogeneous feature selection by group lasso with logistic regression

Authors:

Fei Wu,

Ying Yuan,

Yueting ZhuangAuthors Info & Claims

MM '10: Proceedings of the 18th ACM international conference on Multimedia

Pages 983 - 986

https://doi.org/10.1145/1873951.1874129

Published: 25 October 2010 Publication History

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Abstract

The selection of groups of discriminative features is critical for image understanding since the irrelevant features could deteriorate the performance of image understanding. This paper formulates the selection of groups of discriminative features by the extension of group lasso with logistic regression for high-dimensional feature setting, we call it as the heterogeneous feature selection by Group Lasso with Logistic Regression (GLLR). GLLR encodes a sparse grouping prior to seek after a more interpretable model for feature selection and can identify most of discriminative groups of homogeneous features. The utilization of GLLR for image annotation shows the proposed GLLR achieves a better performance.

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Levin AFreire MOliveira MNastri AHarima LPerdigão-Neto LMagri MFialkovitz GFigueiredo PSiciliano RSabino ECarlotti DRodrigues DNunes FFerreira JFilho GBonfá EUtiyama ESegurado APerondi BMorais AMontal AFusco SFregonesi MRocha MMarcilio IRios IFabiane Kawano Yde Jesus MKallas EMarmo CTanaka Cde Souza HMarchini JCarvalho CFerreira JGuimaraes TLazari CDuarte ABraido MFrancisco PCosta S(2022)Correlating drug prescriptions with prognosis in severe COVID-19: first step towards resource managementBMC Medical Informatics and Decision Making10.1186/s12911-022-01983-722:1Online publication date: 21-Sep-2022
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Heterogeneous feature selection by group lasso with logistic regression
1. Information systems
  1. Information retrieval

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Information & Contributors

Information

Published In

MM '10: Proceedings of the 18th ACM international conference on Multimedia

October 2010

1836 pages

ISBN:9781605589336

DOI:10.1145/1873951

General Chairs:
Alberto del Bimbo
University of Florence, Italy
,
Shih-Fu Chang
Columbia University, USA
,
Program Chair:
Arnold Smeulders
University of Amsterdam, NL

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 October 2010

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MM '10: ACM Multimedia Conference

October 25 - 29, 2010

Firenze, Italy

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Overall Acceptance Rate 995 of 4,171 submissions, 24%

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Cited By

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https://doi.org/10.1186/s12911-022-01983-7
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Zhang HWang JSun ZZurada JPal N(2020)Feature Selection for Neural Networks Using Group Lasso RegularizationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.289326632:4(659-673)Online publication date: 1-Apr-2020
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Song XZhang JHan YJiang J(2016)Semi-supervised feature selection via hierarchical regression for web image classificationMultimedia Systems10.1007/s00530-014-0390-022:1(41-49)Online publication date: 1-Feb-2016
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