L1-Norm-Based Maximum Margin Criterion

doi:10.3969/j.issn.0372-2112.2016.06.018

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L1-Norm-Based Maximum Margin Criterion

更新时间：2025-07-02

- L1-Norm-Based Maximum Margin Criterion
- Acta Electronica Sinica Vol. 44, Issue 6, Pages: 1383-1388(2016)
- 作者机构：
  
  1. 安徽大学计算机科学与技术学院,安徽,合肥,230601
  2. 安徽省工业图像处理与分析重点实验室,安徽,合肥,230039
  3. 安徽大学计算机科学与技术学院,安徽,合肥,230601
  4. 安徽省工业图像处理与分析重点实验室,安徽,合肥,230039
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2016.06.018
  CLC： TP391.4
- Published Online：25 June 2016，
  
  Published：2016
- 稿件说明：
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L1-Norm-Based Maximum Margin Criterion[J]. Acta Electronica Sinica, 2016, 44(6): 1383-1388.
DOI：

L1-Norm-Based Maximum Margin Criterion[J]. Acta Electronica Sinica, 2016, 44(6): 1383-1388. DOI： 10.3969/j.issn.0372-2112.2016.06.018.

摘要

在进行线性投影降维时

由于传统的最大间距准则(Maximum Margin Criterion

MMC)算法基于L2-范数

易于受到野值(outliers)及噪声的影响.该文提出一种基于L1-范数的最大间距准则(L1-norm-based MMC

MMC-L1)降维方法

它充分利用L1-范数对野值及噪声的强鲁棒性以及最大间距准则

提出了一种快速迭代优化算法

并给出了其单调收敛到局部最优的证明.在多个图像数据库上的实验验证了该方法的鲁棒性与高效性.

Abstract

When performing dimensionality reduction with linear projections

maximum margin criterion (MMC) is often affected by outliers and noises due to L2-norm.In this paper

L1-norm-based maximum margin criterion (MMC-L1) is proposed for dimensionality reduction.It makes full use of Maximum Margin Criterion and strong robustness of L1-norm to outliers and noises.A rapid iterative optimization algorithm

with its proof of monotonic convergence to local optimum

is given.Experiments on several public image databases verify the robustness and efficiency of the proposed method.

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