Efficient analysis method for unreliable labeled data

在对诸如生物信号的真实的数据的分析中，给定的标记通常是不可靠的。因为，待测量的对象固有地包含一些物理和生物的不确定性，并且一些标签可能被人类直觉错误地分配。然而，可靠的标签将可用于一小部分样品。在这种情况下，半监督学习方法被有效地应用于分析数据，估计样本的标签值。此外，有利的是，估计的标签值为我们提供了每个样本的置信度。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，以准确地估计标签值作为后验概率。我们称这种方法为逻辑标签传播（LLP）。此外，为了适用于线性线性规划问题，降低计算量，我们提出了一种新的直接利用非线性共轭梯度法的LR优化方法。我们提出的方法实现了更好的估计的置信度和更快的计算时间相比，普通的方法。

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.