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pro vyhledávání: '"low-rank representation"'
Akademický článek
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Publikováno v:
In Knowledge-Based Systems 25 November 2024 304
Autor:
Kong, Jiarui a, Liu, Jingya a, Shang, Ronghua a, ⁎, Zhang, Weitong a, Xu, Songhua b, Li, Yangyang a
Publikováno v:
In Expert Systems With Applications 1 April 2025 267
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 3, Pp 659-673 (2024)
The existing low-rank graph representation algorithms fail to capture the global representation structure of data accurately, and cannot make full use of the valid information of data to guide the construction of the representation graph, then the co
Externí odkaz:
https://doaj.org/article/32ca36ba158d492ba293b7e8cae00d18
Publikováno v:
مهندسی مخابرات جنوب, Vol 11, Iss 43, Pp 27-38 (2024)
Classification of hyperspectral images is one of the most important processes on these images. Hyperspectral images are high dimensional, so classification of these images is difficult. Therefore, methods that extract low-dimensional subspace structu
Externí odkaz:
https://doaj.org/article/191960cad93c446b88d8cffc6234c625
Publikováno v:
IEEE Access, Vol 12, Pp 153664-153675 (2024)
Due to the ability of tensors to maintain the potential structure of complex data and effectively describe high-dimensional data, tensor-based methods have been widely studied and applied. T-product-based representation learning methods have attracte
Externí odkaz:
https://doaj.org/article/ac355e2210684cce8a651d1b88b5b891
Publikováno v:
IEEE Access, Vol 12, Pp 127916-127930 (2024)
Image denoising techniques often rely on convex relaxations, which can introduce bias into estimations. To address this, non-convex regularizers like weighted nuclear norm minimization and weighted Schatten p-norm minimization have been proposed. How
Externí odkaz:
https://doaj.org/article/3038f56a940d436f9511857eb24f6967
Publikováno v:
IEEE Access, Vol 12, Pp 5562-5574 (2024)
Tumor samples clustering based on subspace segmentation is an effective method to discover cancer subtypes. Accurate and reliable identifications of cancer subtypes are crucial for understanding cancer pathogenesis as well as clinical diagnosis and t
Externí odkaz:
https://doaj.org/article/f857446ab4bc4034b0b1452e63eb6499
Akademický článek
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Publikováno v:
Remote Sensing, Vol 16, Iss 16, p 3081 (2024)
Low-rank representation (LRR) is widely utilized in image feature extraction, as it can reveal the underlying correlation structure of data. However, the subspace learning methods based on LRR suffer from the problems of lacking robustness and discri
Externí odkaz:
https://doaj.org/article/19b1b790e4df4f9d9b62db93434cec6d