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·907· 杨梦茵,等:非对称卷积编码器的聚类算法 第5期 things journal,2019,6(2):1856-1865 max聚类算法[.南京大学学报(自然科学版),2020, [8] LECUN Y.BOTTOU L.BENGIO Y.et al.Gradient- 56(4):533-540. based learning applied to document recognition[J].Pro- CHEN Junfen,ZHAO Jiacheng,HAN Jie,et al.Softmax ceedings of the IEEE,1998,86(11):2278-2324. clustering algorithm based on deep features representa- [9] MASCI J,MEIER U,CIRESAN D,et al.Stacked convo- tion[J].Journal of Nanjing university (natural science edi- lutional auto-encoders for hierarchical feature extrac- tion),2020,56(4):533-540. tion[M]//Lecture Notes in Computer Science.Berlin, [21]HE Kaiming,SUN Jian.Convolutional neural networks at Heidelberg:Springer Berlin Heidelberg,2011:52-59. constrained time cost[C]//2015 IEEE Conference on [10]LEE Honglak,EKANADHAM C,NG A Y.Sparse deep Computer Vision and Pattern Recognition.Boston,MA, belief net model for visual area V2[Cl//Proc of Conf on USA.IEEE,2015:5353-5360 Advances in Neural Information Processing Systems. 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[24 ]YANG Jianwei,PARIKH D,BATRA D.Joint unsuper- [13]MA Xiaolei,DAI Zhuang,HE Zhengbing,et al.Learning vised learning of deep representations and image traffic as images:a deep convolutional neural network for clusters[Cl//2016 IEEE Conference on Computer Vision large-scale transportation network speed prediction[J]. and Pattern Recognition.Las Vegas,NV,USA.IEEE. Sensors,2017,17(4):818. 2016:5147-5156 [14]XIE Junyuan,ROSS G,ALI F.Unsupervised deep em- bedding for clustering analysis[C]//Proc of ICML'16 Proc 作者简介: of the 33rd Int Conf on Int Conf on Machine Learning. 杨梦茵,硕土研究生,主要研究方 New York City,NY:Semantic Scholar,2016:478-487. 向为图像聚类和机器学习。 [15]GUO Xifeng,GAO Long,LIU Xinwang,et al.Improved deep embedded clustering with local structure preserva- tion[Cl//IJCAI'17:Proceedings of the 26th International Joint Conference on Artificial Intelligence.New York: ACM2017:1753-1759. [16]YANG Bo.FU Xiao.NICHOLAS D S.et al.Towards K- 陈俊芬,副教授.博士,CCF会员」 means-friendly spaces:simultaneous deep learning and 主要研究方向为数据挖掘、机器学习 clustering[C]//Proc of ICML'17 Proc of the 34th Int Conf 和图像处理。主持河北省留学回国基 on Machine Learning.Sydney,Australia:TonyJebara, 金1项。发表学术论文10余篇。 2016:3861-3870. [17]HUANG Peihao,HUANG Yan,WANG Wei,et al.Deep embedding network for clustering[Cl//2014 22nd Interna- tional Conference on Pattern Recognition.Stockholm, 翟俊海,教授,博士生导师,博士。 Sweden.IEEE.2014:1532-1537. 河北大学学术委员会委员,中国人工 [18]LI Fengfu,QIAO Hong,ZHANG Bo.Discriminatively 智能学会知识工程与分布智能专业委 boosted image clustering with fully convolutional auto- 员会委员,粒计算与知识发现专业委 encoders[J].Pattern recognition,2018,83:161-173 员会委员,主要研究方向为大数据处 [19]VAN L,MAATEN D,GEOFFREY H.Visualizing data 理、机器学习、深度学习。主持省重点 using t-SNE[J].Journal of machine learning research, 自然科学基金项目1项和省自然科学 2008.9(2605):2579-2605. 基金项目2项,近3年发表学术论文 [20]陈俊芬,赵佳成,韩洁,等.基于深度特征表示的Sof 10余篇。things journal, 2019, 6(2): 1856–1865. LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient￾based learning applied to document recognition[J]. Pro￾ceedings of the IEEE, 1998, 86(11): 2278–2324. [8] MASCI J, MEIER U, CIREŞAN D, et al. Stacked convo￾lutional auto-encoders for hierarchical feature extrac￾tion[M]//Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011: 52−59. [9] LEE Honglak, EKANADHAM C, NG A Y. Sparse deep belief net model for visual area V2[C]//Proc of Conf on Advances in Neural Information Processing Systems. Washington D. C. , USA: MIT Press, 2007: 873−880. [10] VINCENT P, LAROCHELLE H, BENGIO Y, et al. Ex￾tracting and composing robust features with denoising au￾toencoders[C]//Proceedings of the 25th international con￾ference on Machine learning-ICML '08. Helsinki, Fin￾land. New York: ACM Press, 2008: 1096−1103. [11] BENGIO Y, LAMBLIN P, POPOVICI D, et al. Greedy layer-wise training of deep networks[C]//Proc of Ad￾vances in Neural Information Processing Systems. Wash￾ington, USA: MIT Press, 2006: 153−160. [12] MA Xiaolei, DAI Zhuang, HE Zhengbing, et al. Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction[J]. Sensors, 2017, 17(4): 818. [13] XIE Junyuan, ROSS G, ALI F. Unsupervised deep em￾bedding for clustering analysis[C]//Proc of ICML’16 Proc of the 33rd Int Conf on Int Conf on Machine Learning. New York City, NY: Semantic Scholar, 2016: 478−487. [14] GUO Xifeng, GAO Long, LIU Xinwang, et al. Improved deep embedded clustering with local structure preserva￾tion[C]//IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence. New York: ACM, 2017: 1753−1759. [15] YANG Bo, FU Xiao, NICHOLAS D S, et al. Towards K￾means-friendly spaces: simultaneous deep learning and clustering[C]//Proc of ICML’17 Proc of the 34th Int Conf on Machine Learning. Sydney, Australia: TonyJebara, 2016: 3861−3870. [16] HUANG Peihao, HUANG Yan, WANG Wei, et al. Deep embedding network for clustering[C]//2014 22nd Interna￾tional Conference on Pattern Recognition. Stockholm, Sweden. IEEE, 2014: 1532−1537. [17] LI Fengfu, QIAO Hong, ZHANG Bo. Discriminatively boosted image clustering with fully convolutional auto￾encoders[J]. Pattern recognition, 2018, 83: 161–173. [18] VAN L, MAATEN D, GEOFFREY H. Visualizing data using t-SNE[J]. Journal of machine learning research, 2008, 9(2605): 2579–2605. [19] [20] 陈俊芬, 赵佳成, 韩洁, 等. 基于深度特征表示的 Soft￾max 聚类算法 [J]. 南京大学学报 (自然科学版), 2020, 56(4): 533–540. CHEN Junfen, ZHAO Jiacheng, HAN Jie, et al. Softmax clustering algorithm based on deep features representa￾tion[J]. Journal of Nanjing university (natural science edi￾tion), 2020, 56(4): 533–540. HE Kaiming, SUN Jian. Convolutional neural networks at constrained time cost[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA. IEEE, 2015: 5353−5360. [21] SONG Chunfeng, LIU Feng, HUANG Yongzhen, et al. Auto-encoder based Data clustering[C]//Iberoamerican Congress on Pattern Recognition. Berlin, Heidelberg: Springer, 2013: 117−124. [22] LIU Hongfu, SHAO Ming, LI Sheng, et al. Infinite en￾semble for image clustering[C]//KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2016: 1745−1754. [23] YANG Jianwei, PARIKH D, BATRA D. Joint unsuper￾vised learning of deep representations and image clusters[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA. IEEE, 2016: 5147−5156. [24] 作者简介: 杨梦茵,硕士研究生,主要研究方 向为图像聚类和机器学习。 陈俊芬,副教授,博士,CCF 会员, 主要研究方向为数据挖掘、机器学习 和图像处理。主持河北省留学回国基 金 1 项。发表学术论文 10 余篇。 翟俊海,教授,博士生导师,博士, 河北大学学术委员会委员,中国人工 智能学会知识工程与分布智能专业委 员会委员、粒计算与知识发现专业委 员会委员,主要研究方向为大数据处 理、机器学习、深度学习。主持省重点 自然科学基金项目 1 项和省自然科学 基金项目 2 项,近 3 年发表学术论文 10 余篇。 ·907· 杨梦茵,等:非对称卷积编码器的聚类算法 第 5 期
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