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LAM3L : Locally adaptive maximum margin metric learning for visual data classification

机译:LAM3L:用于视觉数据分类的局部自适应最大余量度量学习

摘要

Visual data classification, which is aimed at determining a unique label for each class, is an increasingly important issue in the machine learning community. In recent years, increasing attention has been paid to the application of metric learning for classification, which has been proven to be a good way to obtain a promising performance. However, as a result of the limited training samples and data with complex distributions, the vast majority of these algorithms usually fail to perform well. This has motivated us to develop a novel locally adaptive maximum margin metric learning (LAM3L) algorithm in order to maximally separate similar and dissimilar classes, based on the changes between the distances before and after the maximum margin metric learning. The experimental results on two widely used UCI datasets and a real hyperspectral dataset demonstrate that the proposed method outperforms the state-of-the-art metric learning methods.
机译:旨在为每个类别确定唯一标签的视觉数据分类在机器学习社区中正变得日益重要。近年来,人们越来越关注度量学习在分类中的应用,这已被证明是获得有希望的性能的好方法。然而,由于有限的训练样本和具有复杂分布的数据,这些算法中的绝大多数通常无法很好地执行。这促使我们开发一种新颖的局部自适应最大余量度量学习(LAM3L)算法,以便基于最大余量度量学习前后的距离变化最大程度地区分相似和不相似的类。在两个广泛使用的UCI数据集和一个实际的高光谱数据集上的实验结果表明,该方法优于最新的度量学习方法。

著录项

  • 作者

    Dong Y; Du B; Zhang L; Tao D;

  • 作者单位
  • 年度 2017
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类

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