research-article

Self-weighted Robust LDA for Multiclass Classification with Edge Classes

Authors:

Feiping NieAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 12, Issue 1

Article No.: 4, Pages 1 - 19

https://doi.org/10.1145/3418284

Published: 22 December 2020 Publication History

Abstract

Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative features for multi-class classification. A vast majority of existing LDA algorithms are prone to be dominated by the class with very large deviation from the others, i.e., edge class, which occurs frequently in multi-class classification. First, the existence of edge classes often makes the total mean biased in the calculation of between-class scatter matrix. Second, the exploitation of ℓ₂-norm based between-class distance criterion magnifies the extremely large distance corresponding to edge class. In this regard, a novel self-weighted robust LDA with ℓ_2,1-norm based pairwise between-class distance criterion, called SWRLDA, is proposed for multi-class classification especially with edge classes. SWRLDA can automatically avoid the optimal mean calculation and simultaneously learn adaptive weights for each class pair without setting any additional parameter. An efficient re-weighted algorithm is exploited to derive the global optimum of the challenging ℓ_2,1-norm maximization problem. The proposed SWRLDA is easy to implement and converges fast in practice. Extensive experiments demonstrate that SWRLDA performs favorably against other compared methods on both synthetic and real-world datasets while presenting superior computational efficiency in comparison with other techniques.

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Index Terms

Self-weighted Robust LDA for Multiclass Classification with Edge Classes
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

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 12, Issue 1

Regular Papers

February 2021

280 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3436534

Editor:
Yu Zheng
JD Digits, China

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Copyright © 2020 ACM.

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Publication History

Published: 22 December 2020

Accepted: 01 August 2020

Revised: 01 August 2020

Received: 01 December 2019

Published in TIST Volume 12, Issue 1

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Innovation Research Team of Ministry of Education (IRT 17R86)
National Key Research and Development Program of China
National Nature Science Foundation of China
National Natural Science Foundation of China
China Scholarship Council
Innovative Research Group of the National Natural Science Foundation of China
Zhejiang Provincial Natural Science Foundation

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https://doi.org/10.1109/TNNLS.2024.3380020
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https://doi.org/10.1523/JNEUROSCI.2134-23.2024
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