论文标题
具有不同输入维数的数据集的转移学习：线性回归案例的算法和分析
Transfer-Learning Across Datasets with Different Input Dimensions: An Algorithm and Analysis for the Linear Regression Case
论文作者
论文摘要
随着新传感器和监视设备的开发，更多的数据来源可用作机器学习模型的输入。这些可以一方面有助于提高模型的准确性。另一方面，将这些新意见与历史数据相结合仍然是一个挑战，尚未进行足够详细的研究。在这项工作中，我们提出了一种转移学习算法，将新的和历史数据与不同的输入维度相结合。这种方法易于实现，高效，计算复杂性等效于普通最小二乘方法，并且不需要过度参数调整，因此在新数据受到限制时可以直接应用。与其他方法不同，我们提供了一项严格的理论研究，以了解其鲁棒性，这表明它不能胜过仅利用新数据的基线。我们的方法在9个现实生活数据集上实现了最先进的性能，超过了线性DSFT，另一种线性传输学习算法，并且与非线性DSFT相当。
With the development of new sensors and monitoring devices, more sources of data become available to be used as inputs for machine learning models. These can on the one hand help to improve the accuracy of a model. On the other hand, combining these new inputs with historical data remains a challenge that has not yet been studied in enough detail. In this work, we propose a transfer learning algorithm that combines new and historical data with different input dimensions. This approach is easy to implement, efficient, with computational complexity equivalent to the ordinary least-squares method, and requires no hyperparameter tuning, making it straightforward to apply when the new data is limited. Different from other approaches, we provide a rigorous theoretical study of its robustness, showing that it cannot be outperformed by a baseline that utilizes only the new data. Our approach achieves state-of-the-art performance on 9 real-life datasets, outperforming the linear DSFT, another linear transfer learning algorithm, and performing comparably to non-linear DSFT.
