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Publication . Conference object . 2012

Cross-domain representation-learning framework with combination of class-separate and domain-merge objectives

Wenting Tu; Shiliang Sun;
Published: 01 Jan 2012
Publisher: ACM Press
Abstract

Recently, cross-domain learning has become one of the most important research directions in data mining and machine learning. In multi-domain learning, one problem is that the classification patterns and data distributions are different among domains, which leads to that the knowledge (e.g. classification hyperplane) can not be directly transferred from one domain to another. This paper proposes a framework to combine class-separate objectives (maximize separability among classes) and domain-merge objectives (minimize separability among domains) to achieve cross-domain representation learning. Three special methods called DMCS_CSF, DMCS_FDA and DMCS_PCDML upon this framework are given and the experimental results valid their effectiveness.

Subjects by Vocabulary

Microsoft Academic Graph classification: Feature learning Merge (linguistics) Domain (software engineering) Active learning (machine learning) Artificial intelligence business.industry business Class (biology) Discriminative model Machine learning computer.software_genre computer Hyperplane Mathematics Semi-supervised learning

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https://doi.org/10.1145/235133...
Conference object . 2012
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