# Heterogeneous domain adaptation: An unsupervised approach

arXiv: https://arxiv.org/abs/1701.02511 · HTML: https://mlsanity.com/paper/1701.02511/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2017.

## Built on by (most cited first, 17 of 17)

- [Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2206.02465/) (2022)
- [Meta Discovery: Learning to Discover Novel Classes given Very Limited Data](https://mlsanity.com/paper/2102.04002/) (2021)
- [Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift](https://mlsanity.com/paper/2204.03342/) (2022)
- [Generalizing across Temporal Domains with Koopman Operators](https://mlsanity.com/paper/2402.07834/) (2024)
- [Fast and Accurate Transferability Measurement by Evaluating Intra-class Feature Variance](https://mlsanity.com/paper/2308.05986/) (2023)
- [Domain adaptation in small-scale and heterogeneous biological datasets](https://mlsanity.com/paper/2405.19221/) (2024)
- [Vicinal and categorical domain adaptation](https://mlsanity.com/paper/2103.03460/) (2021)
- [Autonomous Cross Domain Adaptation under Extreme Label Scarcity](https://mlsanity.com/paper/2209.01548/) (2022)
- [Transfer learning with generative models for object detection on limited datasets](https://mlsanity.com/paper/2402.06784/) (2024)
- [A Framework for Supervised Heterogeneous Transfer Learning using Dynamic Distribution Adaptation and Manifold Regularization](https://mlsanity.com/paper/2108.12293/) (2021)
- [BioX-Bridge: Model Bridging for Unsupervised Cross-Modal Knowledge Transfer across Biosignals](https://mlsanity.com/paper/2510.02276/) (2025)
- [Heterogeneous Domain Adaptation with Positive and Unlabeled Data](https://mlsanity.com/paper/2304.07955/) (2023)
- [Relating Events and Frames Based on Self-Supervised Learning and Uncorrelated Conditioning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2401.01042/) (2024)
- [AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation](https://mlsanity.com/paper/2404.12635/) (2024)
- [SimMAT: Exploring Transferability from Vision Foundation Models to Any Image Modality](https://mlsanity.com/paper/2409.08083/) (2024)
- [Open-Set Heterogeneous Domain Adaptation: Theoretical Analysis and Algorithm](https://mlsanity.com/paper/2412.13036/) (2024)
- [Domain-Adaptive Model Merging Across Disconnected Modes](https://mlsanity.com/paper/2603.05957/) (2026)

## Abstract (arXiv)

Domain adaptation leverages the knowledge in one domain - the source domain - to improve learning efficiency in another domain - the target domain. Existing heterogeneous domain adaptation research is relatively well-progressed, but only in situations where the target domain contains at least a few labeled instances. In contrast, heterogeneous domain adaptation with an unlabeled target domain has not been well-studied. To contribute to the research in this emerging field, this paper presents: (1) an unsupervised knowledge transfer theorem that guarantees the correctness of transferring knowledge; and (2) a principal angle-based metric to measure the distance between two pairs of domains: one pair comprises the original source and target domains and the other pair comprises two homogeneous representations of two domains. The theorem and the metric have been implemented in an innovative transfer model, called a Grassmann-Linear monotonic maps-geodesic flow kernel (GLG), that is specifically designed for heterogeneous unsupervised domain adaptation (HeUDA). The linear monotonic maps meet the conditions of the theorem and are used to construct homogeneous representations of the heterogeneous domains. The metric shows the extent to which the homogeneous representations have preserved the information in the original source and target domains. By minimizing the proposed metric, the GLG model learns the homogeneous representations of heterogeneous domains and transfers knowledge through these learned representations via a geodesic flow kernel. To evaluate the model, five public datasets were reorganized into ten HeUDA tasks across three applications: cancer detection, credit assessment, and text classification. The experiments demonstrate that the proposed model delivers superior performance over the existing baselines.