Locality Adaptive Discriminant Analysis Framework
| Autor: | Xuelong Li, Mulin Chen, Feiping Nie, Qi Wang |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
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 |
| Zdroj: | IEEE Transactions on Cybernetics. 52:7291-7302 |
| ISSN: | 2168-2275 2168-2267 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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