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Learning an invariant Hilbert space for domain adaptation

Herath, Samitha; Harandi, Mehrtash; Porikli, Fatih

Description

This paper introduces a learning scheme to construct a Hilbert space (i.e., a vector space along its inner product) to address both unsupervised and semi-supervised domain adaptation problems. This is achieved by learning projections from each domain to a latent space along the Mahalanobis metric of the latent space to simultaneously minimizing a notion of domain variance while maximizing a measure of discriminatory power. In particular, we make use of the Riemannian optimization techniques to...[Show more]

CollectionsANU Research Publications
Date published: 2017
Type: Conference paper
URI: http://hdl.handle.net/1885/210273
Source: Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
DOI: 10.1109/CVPR.2017.421

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