Transfer learning via dimensionality reduction

TLDR

Transfer learning enables leveraging abundant labeled data from a source domain to solve related problems in a target domain despite distribution or feature differences. This study investigates transfer learning through dimensionality reduction. The authors learn a low‑dimensional latent space that aligns source and target distributions, project data onto it, and apply standard learning algorithms, with a novel reduction method that minimizes inter‑domain distribution distance. Experiments on indoor WiFi localization and binary text classification demonstrate the approach’s effectiveness.

Abstract

Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the training and testing problems have different distributions or features. In this paper, we consider transfer learning via dimensionality reduction. To solve this problem, we learn a low-dimensional latent feature space where the distributions between the source domain data and the target domain data are the same or close to each other. Onto this latent feature space, we project the data in related domains where we can apply standard learning algorithms to train classification or regression models. Thus, the latent feature space can be treated as a bridge of transferring knowledge from the source domain to the target domain. The main contribution of our work is that we propose a new dimensionality reduction method to find a latent space, which minimizes the distance between distributions of the data in different domains in a latent space. The effectiveness of our approach to transfer learning is verified by experiments in two real world applications: indoor WiFi localization and binary text classification.