Multi-Relevance Transfer Learning

TL;DR

This paper introduces Multi-Relevance Transfer Learning (MRTL), a novel method that simultaneously transfers knowledge from a source domain and leverages relationships among multiple target domains using collective nonnegative matrix tri-factorization.

Contribution

The paper proposes a new MRTL approach that models multiple target domains together, capturing shared latent factors and improving transfer learning performance.

Findings

MRTL outperforms state-of-the-art methods in empirical tests.

The approach effectively captures shared information among multiple domains.

Convergence of the optimization algorithm is guaranteed.

Abstract

Transfer learning aims to faciliate learning tasks in a label-scarce target domain by leveraging knowledge from a related source domain with plenty of labeled data. Often times we may have multiple domains with little or no labeled data as targets waiting to be solved. Most existing efforts tackle target domains separately by modeling the `source-target' pairs without exploring the relatedness between them, which would cause loss of crucial information, thus failing to achieve optimal capability of knowledge transfer. In this paper, we propose a novel and effective approach called Multi-Relevance Transfer Learning (MRTL) for this purpose, which can simultaneously transfer different knowledge from the source and exploits the shared common latent factors between target domains. Specifically, we formulate the problem as an optimization task based on a collective nonnegative matrix tri-factorization framework. The proposed approach achieves both source-target transfer and target-target leveraging by sharing multiple decomposed latent subspaces. Further, an alternative minimization learning algorithm is developed with convergence guarantee. Empirical study validates the performance and effectiveness of MRTL compared to the state-of-the-art methods.