Abstract
The manifold tangent space-based algorithm has emerged as a promising approach for processing and recognizing high-dimensional data. In this study, we propose a new algorithm based on the manifold tangent space, called the manifold tangent space-based 2D-DLPP algorithm. This algorithm embeds the covariance matrix into the tangent space of the SPD manifold and utilizes Log-Euclidean Metric Learning (LEM) to fully extract feature information, thus enhancing the discriminative ability of 2D-DLPP. Comparative experiments were conducted to evaluate the algorithm, and the results showed superior recognition ability compared to other existing algorithms. Experiments also demonstrate that the algorithm can retain the local nonlinear structure of the manifold and improve the class separability of samples.
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This work was supported by Key Research and Development Plans of Guangxi Province (Granted No.AB22080077).
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Li, X., Li, B., Wang, Z. (2023). 2D-DLPP Algorithm Based on SPD Manifold Tangent Space. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14089. Springer, Singapore. https://doi.org/10.1007/978-981-99-4752-2_17
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DOI: https://doi.org/10.1007/978-981-99-4752-2_17
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