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International Journal of Computer Vision

Erschienen in:

01.08.2014

Generalized Transfer Subspace Learning Through Low-Rank Constraint

verfasst von: Ming Shao, Dmitry Kit, Yun Fu

Erschienen in: International Journal of Computer Vision | Ausgabe 1-2/2014

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Abstract

It is expensive to obtain labeled real-world visual data for use in training of supervised algorithms. Therefore, it is valuable to leverage existing databases of labeled data. However, the data in the source databases is often obtained under conditions that differ from those in the new task. Transfer learning provides techniques for transferring learned knowledge from a source domain to a target domain by finding a mapping between them. In this paper, we discuss a method for projecting both source and target data to a generalized subspace where each target sample can be represented by some combination of source samples. By employing a low-rank constraint during this transfer, the structure of source and target domains are preserved. This approach has three benefits. First, good alignment between the domains is ensured through the use of only relevant data in some subspace of the source domain in reconstructing the data in the target domain. Second, the discriminative power of the source domain is naturally passed on to the target domain. Third, noisy information will be filtered out during knowledge transfer. Extensive experiments on synthetic data, and important computer vision problems such as face recognition application and visual domain adaptation for object recognition demonstrate the superiority of the proposed approach over the existing, well-established methods.

Vorheriger Artikel Harnessing Lab Knowledge for Real-World Action Recognition

Nächster Artikel Domain Adaptation for Face Recognition: Targetize Source Domain Bridged by Common Subspace

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Titel: Generalized Transfer Subspace Learning Through Low-Rank Constraint
verfasst von: Ming Shao
Dmitry Kit
Yun Fu
Publikationsdatum: 01.08.2014
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 1-2/2014
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-014-0696-6

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