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Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds

Authors:

Balaji Krishnapuram,

Lawrence Carin,

Mario A. T. Figueiredo,

Alexander J. HarteminkAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 27, Issue 6

Pages 957 - 968

https://doi.org/10.1109/TPAMI.2005.127

Published: 01 June 2005 Publication History

Abstract

Recently developed methods for learning sparse classifiers are among the state-of-the-art in supervised learning. These methods learn classifiers that incorporate weighted sums of basis functions with sparsity-promoting priors encouraging the weight estimates to be either significantly large or exactly zero. From a learning-theoretic perspective, these methods control the capacity of the learned classifier by minimizing the number of basis functions used, resulting in better generalization. This paper presents three contributions related to learning sparse classifiers. First, we introduce a true multiclass formulation based on multinomial logistic regression. Second, by combining a bound optimization approach with a component-wise update procedure, we derive fast exact algorithms for learning sparse multiclass classifiers that scale favorably in both the number of training samples and the feature dimensionality, making them applicable even to large data sets in high-dimensional feature spaces. To the best of our knowledge, these are the first algorithms to perform exact multinomial logistic regression with a sparsity-promoting prior. Third, we show how nontrivial generalization bounds can be derived for our classifier in the binary case. Experimental results on standard benchmarkdata sets attest to the accuracy, sparsity, and efficiency of the proposed methods.

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Hu XYang J(2024)Group Penalized Multinomial Logit Models and Stock Return Direction PredictionIEEE Transactions on Information Theory10.1109/TIT.2024.337675170:6(4297-4318)Online publication date: 13-Mar-2024
https://dl.acm.org/doi/10.1109/TIT.2024.3376751
Qu JXiao LDong WLi Y(2024)MTLSC-DiffKnowledge-Based Systems10.1016/j.knosys.2024.112415303:COnline publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.112415
Hu XYang J(2024)G-LASSO/G-SCAD/G-MCP penalized trinomial logit dynamic models predict up trends, sideways trends and down trends for stock returns▪Expert Systems with Applications: An International Journal10.1016/j.eswa.2024.123476249:PAOnline publication date: 1-Sep-2024
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Index Terms

Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
      2. Factorization methods
        Canonical correlation analysis
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Regression analysis

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cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 27, Issue 6

June 2005

176 pages

ISSN:0162-8828

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Copyright © 2005.

Publisher

IEEE Computer Society

United States

Publication History

Published: 01 June 2005

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https://dl.acm.org/doi/10.1109/TIT.2024.3376751
Qu JXiao LDong WLi Y(2024)MTLSC-DiffKnowledge-Based Systems10.1016/j.knosys.2024.112415303:COnline publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.112415
Hu XYang J(2024)G-LASSO/G-SCAD/G-MCP penalized trinomial logit dynamic models predict up trends, sideways trends and down trends for stock returns▪Expert Systems with Applications: An International Journal10.1016/j.eswa.2024.123476249:PAOnline publication date: 1-Sep-2024
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