Unsupervised domain adaptation using eigenanalysis in kernel space for categorisation tasks
Autor: | Sukhendu Das, Suranjana Samanta |
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Rok vydání: | 2015 |
Předmět: |
Machine learning
computer.software_genre Measure (mathematics) Domain (software engineering) symbols.namesake Linear transformations Target domain Electrical and Electronic Engineering Reproducing Kernel Hilbert spaces Eigenvalues and eigenvectors Mathematics Domain adaptation Eigenvalues and eigenfunctions Metadata Recent state business.industry Non-linear transformations Real-world datasets Hilbert space Pattern recognition Transformation (function) Kernel (statistics) Signal Processing symbols Unsupervised learning Mathematical transformations Computer Vision and Pattern Recognition Artificial intelligence business computer Eigenanalysis Eigenvectors and eigenvalues Software Reproducing kernel Hilbert space |
Zdroj: | IET Image Processing. 9:925-930 |
ISSN: | 1751-9667 |
DOI: | 10.1049/iet-ipr.2014.0754 |
Popis: | This study describes a new technique of unsupervised domain adaptation based on eigenanalysis in kernel space, for the purpose of categorisation tasks. The authors propose a transformation of data in source domain, such that the eigenvectors and eigenvalues of the transformed source domain become similar to that of the target domain. They extend this idea to the reproducing kernel Hilbert space, which enables to deal with non-linear transformation of source domain. They also propose a measure to obtain the appropriate number of eigenvectors needed for transformation. Results on object, video and text categorisations tasks using real-world datasets show that the proposed method produces better results when compared with a few recent state-of-art methods of domain adaptation. � 2015 The Institution of Engineering and Technology. |
Databáze: | OpenAIRE |
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