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...

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التفاصيل البيبلوغرافية

الحاوية / القاعدة:	Applied Sciences
المؤلفون الرئيسيون:	Zhichao Song, Meiling Chen, Liang Xie, Xi Fang
التنسيق:	مقال
اللغة:	الإنجليزية
منشور في:	MDPI AG 2025-05-01
الموضوعات:	unsupervised feature selection autoencoder dual-graph regularization norm [<sup>68</sup>Ga]Ga-Pentixafor PET
الوصول للمادة أونلاين:	https://www.mdpi.com/2076-3417/15/11/6177