Efficient analysis method for unreliable labeled data

不可靠标记数据的高效分析方法

• 批准号: 22700191
• 负责人: WATANABE Kenji
• 金额: $ 1.41万
• 依托单位: National Institute of Advanced Industrial Science and Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Young Scientists (B)
• 财政年份: 2010
• 资助国家: 日本
• 起止时间: 2010 至 2011
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-22700191/
• 关键词: ロジスティック回帰; 数量化IV類; 半教師あり機械学習手法; 最適化; 統計的パターン認識; 機械学習手法; 多変量解析; 付与ラベルの確信度推定手法

In the analysis of real data such as the biological signals, the given labels are often unreliable. Because, objects to be measured inherently contain some physical and biological uncertainty, and some labels might be incorrectly assigned by human intuition. Whereas, reliable labels would be available for a small portion of the samples. In such case, a semi-supervised learning method is effectively applied to analyze the data, estimating the label values of samples. In addition, it is favorable that the estimated label values provide us the degree of confidence of each sample. In this research, we proposed a novel method of semi-supervised learning, incorporating logistic functions into label propagation in order to accurately estimate the label values as the posterior probabilities. We call this method logistic label propagation(LLP). In addition, we proposed a novel optimization method for LR by directly using the non-linear conjugate gradient method in order to apply to LLP and to reduce the computational cost. Our proposed methods achieve the better estimation of degree of confidence and the faster computation times compared with the ordinary methods.

在对生物信号等真实数据的分析中，给定的标签通常是不可靠的。因为，被测量的对象本身就包含一些物理和生物的不确定性，并且人类的直觉可能会错误地分配一些标签。然而，一小部分样品可以获得可靠的标签。在这种情况下，有效地应用半监督学习方法来分析数据，估计样本的标签值。此外，估计的标签值为我们提供每个样本的置信度也是有利的。在这项研究中，我们提出了一种新的半监督学习方法，将逻辑函数纳入标签传播中，以便准确估计标签值作为后验概率。我们将此方法称为逻辑标签传播（LLP）。此外，我们提出了一种直接使用非线性共轭梯度法的LR优化方法，以应用于LLP并降低计算成本。与普通方法相比，我们提出的方法实现了更好的置信度估计和更快的计算时间。

项目成果

Logistic Label Propagation for Semi-supervised Learning

用于半监督学习的逻辑标签传播

• 发表时间: 2010
• 作者: Kenji Watanabe; Takumi Kobayashi;Nobuyuki Otsu
• 通讯作者: Nobuyuki Otsu

Logistic Label Propagation for Semi-supervised Learning

用于半监督学习的逻辑标签传播

• DOI: 10.1007/978-3-642-17537-4_57
• 发表时间: 2010
• 作者: Kenji Watanabe; Takumi Kobayashi;Nobuyuki Otsu
• 通讯作者: Nobuyuki Otsu

Logistic Label Propagation for Semi-supervised Learning

用于半监督学习的逻辑标签传播

• 发表时间: 2010
• 作者: Kenji Watanabe; Takumi Kobayashi; Nobuyuki Otsu
• 通讯作者: Nobuyuki Otsu