Unsupervised Feature Selection via a Dual-Graph Autoencoder with <inline-formula><math display="inline"><semantics><mrow><msub><mi mathvariant="bold-script">l</mi><mrow><mn mathvariant="bold">2</mn><mo mathvariant="bold">,</mo><mn mathvariant="bold">1</mn><mo mathvariant="bold">/</mo><mn mathvariant="bold">2</mn></mrow></msub></mrow></semantics></math></inline-formula>-Norm for [⁶⁸Ga]Ga-Pentixafor PET Imaging of Glioma

In the era of big data, high-dimensional datasets have become increasingly common in fields such as biometrics, computer vision, and medical imaging. While such data contain abundant information, they are often accompanied by substantial noise, high redundancy, and complex intrinsic structures, posi...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Zhichao Song, Meiling Chen, Liang Xie, Xi Fang
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	MDPI AG 2025-05-01
Sarja:	Applied Sciences
Aiheet:	unsupervised feature selection autoencoder dual-graph regularization norm [<sup>68</sup>Ga]Ga-Pentixafor PET
Linkit:	https://www.mdpi.com/2076-3417/15/11/6177
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