ABSTRACT

One of the most important assumptions in machine learning tasks is the fact that training data points and test data points are extracted from the same distribution. However, this paper assumes the situation in which this fact does no longer hold. Therefore, a task named space adjustment, through which the distribution of the data points in the training-data space and the distribution of the data points in the test-data space become identical, is inevitable. Hereby, authors propose a linear mapping for the space adjustment task in the paper. It considers four approaches for preserving localities among data samples during the space adjustment. Each approach is defined based on a different locality concept. Considering all locality concepts in an objective function, authors transform the space adjustment into an optimisation problem. The paper proposes to optimise the corresponding objective function by an iterative approach. Empirical study shows that the proposed method outperforms the baseline methods. To do experiments, authors employ a large number of real-world datasets.

摘要

机器学习任务中最重要的假设之一是训练数据点和测试数据点是从同一分布中提取的。然而，本文假设了这一事实不再成立的情况。因此，一个名为空间调整的任务是不可避免的，通过该任务，训练数据空间中数据点的分布和测试数据空间中数据点的分布变得相同。因此，作者在论文中提出了空间调整任务的线性映射。它考虑了在空间调整期间保留数据样本之间的位置的四种方法。每种方法都是根据不同的位置概念定义的。考虑到目标函数中的所有局部性概念，作者将空间调整转化为优化问题。论文提出通过迭代的方式优化相应的目标函数。实证研究表明，所提出的方法优于基线方法。为了进行实验，作者使用了大量真实世界的数据集。