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Discriminant featur...
Discriminant feature extraction by generalized difference subspace
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- Fukui, Kazuhiro (författare)
- Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, Ibaragi, Japan
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- Sogi, Naoya (författare)
- Department of Computer science, University of Tsukuba, 13121 Tsukuba, Ibaragi, Japan
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- Kobayashi, Takumi (författare)
- Information Technology Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaragi, Japan
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- Xue, Jing-Hao (författare)
- Department of Statistical Science, University College London, London, London, United Kingdom of Great Britain and Northern Ireland
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- Maki, Atsuto (författare)
- KTH,Skolan för elektroteknik och datavetenskap (EECS)
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2023
- 2023
- Engelska.
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : Institute of Electrical and Electronics Engineers (IEEE). - 0162-8828 .- 1939-3539. ; 45:2, s. 1618-1635
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- This paper reveals the discriminant ability of the orthogonal projection of data onto a generalized difference subspace (GDS) both theoretically and experimentally. In our previous work, we have demonstrated that GDS projection works as the quasi-orthogonalization of class subspaces. Interestingly, GDS projection also works as a discriminant feature extraction through a similar mechanism to the Fisher discriminant analysis (FDA). A direct proof of the connection between GDS projection and FDA is difficult due to the significant difference in their formulations. To avoid the difficulty, we first introduce geometrical Fisher discriminant analysis (gFDA) based on a simplified Fisher criterion. gFDA can work stably even under few samples, bypassing the small sample size (SSS) problem of FDA. Next, we prove that gFDA is equivalent to GDS projection with a small correction term. This equivalence ensures GDS projection to inherit the discriminant ability from FDA via gFDA. Furthermore, we discuss two useful extensions of these methods, 1) nonlinear extension by kernel trick, 2) the combination of convolutional neural network (CNN) features. The equivalence and the effectiveness of the extensions have been verified through extensive experiments on the extended Yale B+, CMU face database, ALOI, ETH80, MNIST and CIFAR10, focusing on the SSS problem. IEEE
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Discriminant analysis
- Face recognition
- Feature extraction
- Fisher criterion
- Image recognition
- Kernel
- Lighting
- PCA without data centering
- Principal component analysis
- subspace representation
- Task analysis
- Extraction
- Fisher information matrix
- Job analysis
- Neural networks
- Discriminant feature extraction
- Features extraction
- Fisher discriminant analysis
- Principal-component analysis
- Subspace projection
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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