TITLE: Transfer-Learning Across Datasets with Different Input Dimensions: An Algorithm and Analysis for the Linear Regression Case 

ABSTRACT: 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
however, 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 the new and the historical data, that
is especially beneficial when the new data is scarce. We focus the approach on
the linear regression case, which allows us to conduct a rigorous theoretical
study on the benefits of the approach. We show that our approach is robust
against negative transfer-learning, and we confirm this result empirically with
real and simulated data.
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
however, 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 the new and the historical data, that
is especially beneficial when the new data is scarce. We focus the approach on
the linear regression case, which allows us to conduct a rigorous theoretical
study on the benefits of the approach. We show that our approach is robust
against negative transfer-learning, and we confirm this result empirically with
real and simulated data.
Transfer-Learning Across Datasets with Different Input Dimensions: An Algorithm and Analysis for the Linear Regression Case
http://arxiv.org/abs/2202.05069v2