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A general non-parametric active learning framework for classification on multiple manifolds

机译:用于多个流形分类的通用非参数主动学习框架

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摘要

Active learning is an important paradigm for investigating learners' behavior and reducing costs on labeling. We propose a novel non-parametric active learning framework which utilizes label propagation to sense the potential data clusters/manifolds in the feature space and minimizes global uncertainty to investigate the unexplored clusters/manifolds for querying examples. Based on this framework, it is convenient to design new active learning algorithms for targeted problems. Furthermore, we analyze the sample selection mechanism of our proposed method and provide a formal proof. While selecting informative examples, our method has the following characteristics: (1) in each iteration, examples are primarily chosen from the cluster which contains unlabeled samples; (2) if there is more than one cluster with unlabeled samples, it will choose from the one containing the most samples; (3) the example which has the closest connection with the others will be preferentially selected for the same cluster. The designed algorithms achieve empirical success in multi-class classification and dramatically reduce the label costs on several real world datasets. (C) 2019 Elsevier B.V. All rights reserved.
机译:主动学习是调查学习者行为并降低标签成本的重要范例。我们提出了一种新颖的非参数主动学习框架,该框架利用标签传播来感知特征空间中的潜在数据簇/流形,并最大程度地减少全局不确定性,以调查未探索的簇/流形以查询示例。基于此框架,可以方便地针对目标问题设计新的主动学习算法。此外,我们分析了我们提出的方法的样本选择机制,并提供了形式证明。在选择信息丰富的示例时,我们的方法具有以下特点:(1)在每次迭代中,示例主要是从包含未标记样本的聚类中选择的; (2)如果未标记样本的簇多于一个,则从包含最多样本的簇中选择; (3)对于同一集群,将优先选择与其他关系最紧密的示例。设计的算法在多类分类中取得了经验上的成功,并显着降低了一些实际数据集上的标签成本。 (C)2019 Elsevier B.V.保留所有权利。

著录项

  • 来源
    《Pattern recognition letters》 |2020年第2期|250-258|共9页
  • 作者

  • 作者单位

    Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China|Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates;

    Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China;

    Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China|Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Beijing Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Active learning; Multi-class classification; Label propagation; Non-parametric;

    机译:主动学习;多类分类;标签传播;非参数;

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