Revisiting (ε, γ, τ)-similarity learning for domain adaptation
Year: 2018
Type: conference-paper
Abstract: Similarity learning is an active research area in machine learning that tackles the problem of finding a similarity function tailored to an observable data sample in order to achieve efficient classification. This learning scenario has been generally formalized by the means of a (, γ, τ)−good similarity learning framework in the context of supervised classification and has been shown to have important theoretical guarantees. In this paper , we propose to extend the theoretical analysis of similarity learning to the domain adaptation setting, a particular situation occurring when the similarity is learned and then deployed on samples following different probability distributions. We give a new definition of an (, γ)−good similarity for domain adaptation and prove several results quantifying the performance of a similarity function on a target domain after it has been trained on a source domain. We particularly show that if the source domain support contains that of the target then a notable improvement of the adaptation is achievable.
Source: HAL
Authors: Dhouib, Sofien, Redko, Ievgen
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2890533838