Transfer Learning via Latent Dependency Factor for Estimating PM 2.5
Shrey Gupta Yongbee Park Jianzhao Bi Suyash Gupta Andreas Züfle Avani Wildani Yang Liu
Air pollution, especially particulate matter 2.5 (PM 2.5), is a pressing
concern for public health and is difficult to estimate in developing countries
(data-poor regions) due to a lack of ground sensors. Transfer learning models
can be leveraged to solve this problem, as they use alternate data sources to
gain knowledge (i.e., data from data-rich regions). However, current transfer
learning methodologies do not account for dependencies between the source and
the target domains. We recognize this transfer problem as spatial transfer
learning and propose a new feature named Latent Dependency Factor (LDF) that
captures spatial and semantic dependencies of both domains and is subsequently
added to the datasets. We generate LDF using a novel two-stage autoencoder
model that learns from clusters of similar source and target domain data. Our
experiments show that transfer models using LDF have a $19.34\%$ improvement
over the best-performing baselines. We additionally support our experiments
with qualitative results.