徐葉松,男,碩導(dǎo)。2022年3月畢業(yè)于南京理工大學(xué)計(jì)算機(jī)科學(xué)與工程學(xué)院,并獲計(jì)算機(jī)科學(xué)與技術(shù)專業(yè)工學(xué)博士學(xué)位。2022年4月入職于安徽工程大學(xué)計(jì)算機(jī)與信息學(xué)院。主要研究方向?yàn)闄C(jī)器學(xué)習(xí)、模式識(shí)別和計(jì)算機(jī)視覺。
主持的項(xiàng)目:
1. 面向大規(guī)模復(fù)雜數(shù)據(jù)的子空間聚類算法研究,國家自然科學(xué)基金青年項(xiàng)目。2024.01-2026.12.
2. 針對(duì)大規(guī)模數(shù)據(jù)的多視圖聚類算法研究,安徽省教育廳高校科學(xué)研究重點(diǎn)項(xiàng)目。2023.09-2025.08.
第一作者發(fā)表的論文:
1. Auto-Encoder-Based Latent Block Diagonal Representation for Subspace Clustering, IEEE Transactions on Cybernetics, 2020. (SCI一區(qū),TOP)
2. Learnable Low-Rank Latent Dictionary for Subspace Clustering,Pattern Recognition, 2021. (SCI一區(qū),TOP)
3. Linearity-Aware Subspace Clustering, AAAI(Oral), 2022. (CCF-A會(huì)議,人工智能領(lǐng)域頂會(huì))
4. Fast SubspaceClustering by Learning Projective Block Diagonal Representation,Pattern Recognition, 2023. (SCI一區(qū),TOP)
5. Sparseness and Correntropy-Based Block Diagonal Representation for Robust Subspace Clustering, IEEE Signal Processing Letters, 2024. (SCI二區(qū), CCF-C)
6. Asymptotics-Aware Multi-View Subspace Clustering, IEEE Transactions on Multimedia, 2025. (SCI一區(qū),TOP)
7. Metric Learning-Based Subspace Clustering, IEEE Transactions on Neural Networks and Learning Systems, 2025. (SCI一區(qū),TOP)
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