基于协方差矩阵的主动学习方法及应用研究

由于工业过程采集的数据中常包含大量的无标签样本,而有标签样本数量少且人工标记成本较高,因此,提出一种基于协方差矩阵的主动学习方法.利用有标签样本建立高斯过程回归模型,并构建无标签样本之间的协方差矩阵,以协方差矩阵行列式的值作为评价指标.在挑选信息量较大的无标签样本的同时,衡量样本间的相似性,避免样本的冗余添加,最终在相...

Research on Active Learning Method and Application Based on Covariance Matrix

Since the data collected from industrial processes often contain a large number of unlabeled samples, while the number of labeled samples is small and the cost of manual labeling is high, an active learning method based on covariance matrix is proposed. This method uses labeled samples to establish a Gaussian process regression model, and constructs the covariance matrix between the unlabeled samples, using the value of the determinant of the covariance matrix as an evaluation indicator. While selecting informative unlabeled samples, the similarity between samples is measured to avoid redundant addition of samples, which finally improves model prediction accuracy at the same labeling cost. The application simulation of the algorithm based on industrial process data verifies the feasibility and effectiveness of the proposed method.