Locality Adaptive Discriminant Analysis Framework
| العنوان: | Locality Adaptive Discriminant Analysis Framework |
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| المؤلفون: | Xuelong Li, Mulin Chen, Feiping Nie, Qi Wang |
| المصدر: | IEEE Transactions on Cybernetics. 52:7291-7302 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Computer science, Gaussian, Matrix representation, 02 engineering and technology, Pattern Recognition, Automated, symbols.namesake, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Projection (set theory), business.industry, Dimensionality reduction, 020208 electrical & electronic engineering, Locality, Discriminant Analysis, Pattern recognition, Linear discriminant analysis, Computer Science Applications, Human-Computer Interaction, Control and Systems Engineering, symbols, 020201 artificial intelligence & image processing, Artificial intelligence, Noise (video), business, Algorithms, Software, Subspace topology, Information Systems |
| الوصف: | Linear discriminant analysis (LDA) is a well-known technique for supervised dimensionality reduction and has been extensively applied in many real-world applications. LDA assumes that the samples are Gaussian distributed, and the local data distribution is consistent with the global distribution. However, real-world data seldom satisfy this assumption. To handle the data with complex distributions, some methods emphasize the local geometrical structure and perform discriminant analysis between neighbors. But the neighboring relationship tends to be affected by the noise in the input space. In this research, we propose a new supervised dimensionality reduction method, namely, locality adaptive discriminant analysis (LADA). In order to directly process the data with matrix representation, such as images, the 2-D LADA (2DLADA) is also developed. The proposed methods have the following salient properties: 1) they find the principle projection directions without imposing any assumption on the data distribution; 2) they explore the data relationship in the desired subspace, which contains less noise; and 3) they find the local data relationship automatically without the efforts for tuning parameters. The performance of dimensionality reduction shows the superiorities of the proposed methods over the state of the art. |
| تدمد: | 2168-2275 2168-2267 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::52303912b83335d3633222548feb2855 https://doi.org/10.1109/tcyb.2021.3049684 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi.dedup.....52303912b83335d3633222548feb2855 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21682275 21682267 |
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