第4期 张敏，等：结合度量融合和地标表示的自编码谱聚类算法 ·695· 文算法聚类精度受地标点个数影响较大，且选取 ing:incremental perspective and novel analysis[J].ACM 方式略微增加了算法复杂度，未来将致力于寻求 transactions on knowledge discovery from data,2016, 更为有效的地标点选择方式，在保证聚类精度的 11(11-25. 同时进一步降低算法复杂度。 [12]邓思宇，刘福伦，黄雨婷，等.基于PageRank的主动学 习算法).智能系统学报，2019,14(3)551-559. 参考文献： DENG Siyu,LIU Fulun,HUANG Yuting,et al.Active [1]WANG Lijuan,DING Shifei,JIA Hongjie.An improve- learning through PageRank[J.CAAl transactions on in- ment of spectral clustering via message passing and telligent systems,2019,14(3):551-559. density sensitive similarity[J].IEEE access,2019,7: [13]RAFAILID D.CONSTANTINOU E,MANOLO- 101054-101062 POULOS Y.Landmark selection for spectral clustering [2]LI Xinning,ZHAO Xiaoxiao,CHU Derun,et al.An au- based on weighted PageRank[J].Future generation com- toencoder-based spectral clustering algorithm[J].Soft com- puter systems,2017,68:465-472. puting,2020,24(3:1661-1671. [14]LIU Li,SUN Letian,CHEN Shiping,et al.K-PRSCAN: [3]王一宾，李田力，程玉胜.结合谱聚类的标记分布学 A clustering method based on PageRank[J].Neurocom- puting,2016,175:65-80. 习[.智能系统学报，2019,145)：966-973. WANG Yibin,LI Tianli,CHENG Yusheng.Label distri- [15]JIA Hongjie,DING Shifei,DU Mingjing,et al.Approx- imate normalized cuts without eigen-decomposition[J]. bution learning based on spectral clustering[J].CAAI Information sciences,2016,374:135-150. transactions on intelligent systems,2019,14(5):966-973. [4]赵晓晓，周治平.结合稀疏表示与约束传递的半监督谱 [16]TIAN Fei,GAO Bin,CUI Qing,et al.Learning deep rep- resentations for graph clustering[C]//Proceedings of the 聚类算法)智能系统学报，2018,13(5)855-863. 28th AAAI Conference on Artificial Intelligence.Quebec. ZHAO Xiaoxiao,ZHOU Zhiping.A semi-supervised spec- Canada,2014:1293-1299. tral clustering algorithm combined with sparse representa- [17]BANIJAMALI E,GHODSI A.Fast spectral clustering us- tion and constraint propagation[J].CAAI transactions on ing autoencoders and landmarks[C]//Proceedings of Inter- intelligent systems,2018,13(5):855-863. national Conference Image Analysis and Recognition. [5]LANGONE R,SUYKENS J A K.Fast kernel spectral Montreal,Canada,2017:380-388. clustering[J].Neurocomputing,2017,268:27-33. [18]光俊叶，邵伟，孙亮，等.基于融合欧氏距离与Kendall [6]ZHAN Qiang,MAO Yu.Improved spectral clustering Tau距离度量的谱聚类算法U.控制理论与应用，2017 based on Nystrom method[J].Multimedia tools and applic- 346):783-789 ations,.2017,76(19):20149-20165. GUANG Junye,SHAO Wei,SUN Liang,et al.Spectral [7]YANG Xiaojun,YU Weizhong,WANG Rong,et al.Fast clustering with mixed Euclidean and Kendall Tau spectral clustering learning with hierarchical bipartite metrics[J].Control theory applications,2017,34(6): graph for large-scale data[J].Pattern recognition letters, 783-789 2020,130(2:345-352. [19]WEI Kai,TIAN Pingfang,GU Jingguang,et al.RDF data [8]CHEN Xinlei,CAI Deng.Large scale spectral clustering assessment based on metrics and improved PageRank al- with landmark-based representation[C]//Proceedings of the gorithm[C]//Proceedings of International Conference on 24hAAAI Conference on Artificial Intelligence.San Fran- Database Systems for Advanced Applications.Suzhou, cisco,.USA,2011:313-318. China.2017:204-212. [9]CAI Deng,CHEN Xinlei.Large scale spectral clustering [20]谢娟英，丁丽娟.完全自适应的谱聚类算法).电子学 via landmark-based sparse representation[J].IEEE trans 报，2019,47(5)：1000-1008. cybern,2015,45(8):1669-1680. XIE Juanying,DING Lijuan.The true self-adaptive spec- [10们叶茂，刘文芬.基于快速地标采样的大规模谱聚类算 tral clustering algorithms[J].Acta electronica sinica, 法[.电子与信息学报，2017,39(2)：278-284 2019,47(5:1000-1008. YE Mao,LIU Wenfen.Large scale spectral clustering [21]NG A Y,JORDAN M I,WEISS Y.On spectral cluster- based on fast landmark sampling[J].Journal of electron- ing:analysis and an algorithm[C]//Proceedings of Neural ics and information technology,2017,39(2):278-284. Information Processing Systems 14,NIPS 2001.Van- [11]ZHANG Xianchao,ZONG Linlin,YOU Quanzeng,et al. couver,British Columbia,Canada,2002:849-856. Sampling for Nystrom extension-based spectral cluster- [22]XIE Juanying,ZHOU Ying,DING Lijuan.Local stand-文算法聚类精度受地标点个数影响较大，且选取 方式略微增加了算法复杂度，未来将致力于寻求 更为有效的地标点选择方式，在保证聚类精度的 同时进一步降低算法复杂度。 参考文献： WANG Lijuan, DING Shifei, JIA Hongjie. An improve￾ment of spectral clustering via message passing and density sensitive similarity[J]. IEEE access, 2019, 7: 101054–101062. [1] LI Xinning, ZHAO Xiaoxiao, CHU Derun, et al. An au￾toencoder-based spectral clustering algorithm[J]. Soft com￾puting, 2020, 24(3): 1661–1671. [2] 王一宾, 李田力, 程玉胜. 结合谱聚类的标记分布学 习 [J]. 智能系统学报, 2019, 14(5): 966–973. WANG Yibin, LI Tianli, CHENG Yusheng. Label distri￾bution learning based on spectral clustering[J]. CAAI transactions on intelligent systems, 2019, 14(5): 966–973. [3] 赵晓晓, 周治平. 结合稀疏表示与约束传递的半监督谱 聚类算法 [J]. 智能系统学报, 2018, 13(5): 855–863. ZHAO Xiaoxiao, ZHOU Zhiping. A semi-supervised spec￾tral clustering algorithm combined with sparse representa￾tion and constraint propagation[J]. CAAI transactions on intelligent systems, 2018, 13(5): 855–863. [4] LANGONE R, SUYKENS J A K. Fast kernel spectral clustering[J]. Neurocomputing, 2017, 268: 27–33. [5] ZHAN Qiang, MAO Yu. Improved spectral clustering based on Nyström method[J]. Multimedia tools and applic￾ations, 2017, 76(19): 20149–20165. [6] YANG Xiaojun, YU Weizhong, WANG Rong, et al. Fast spectral clustering learning with hierarchical bipartite graph for large-scale data[J]. Pattern recognition letters, 2020, 130(2): 345–352. [7] CHEN Xinlei, CAI Deng. Large scale spectral clustering with landmark-based representation[C]//Proceedings of the 24th AAAI Conference on Artificial Intelligence. San Fran￾cisco, USA, 2011: 313−318. [8] CAI Deng, CHEN Xinlei. Large scale spectral clustering via landmark-based sparse representation[J]. IEEE trans cybern, 2015, 45(8): 1669–1680. [9] 叶茂, 刘文芬. 基于快速地标采样的大规模谱聚类算 法 [J]. 电子与信息学报, 2017, 39(2): 278–284. YE Mao, LIU Wenfen. Large scale spectral clustering based on fast landmark sampling[J]. Journal of electron￾ics and information technology, 2017, 39(2): 278–284. [10] ZHANG Xianchao, ZONG Linlin, YOU Quanzeng, et al. Sampling for Nyström extension-based spectral cluster- [11] ing: incremental perspective and novel analysis[J]. ACM transactions on knowledge discovery from data, 2016, 11(1): 1–25. 邓思宇, 刘福伦, 黄雨婷, 等. 基于 PageRank 的主动学 习算法 [J]. 智能系统学报, 2019, 14(3): 551–559. DENG Siyu, LIU Fulun, HUANG Yuting, et al. Active learning through PageRank[J]. CAAI transactions on in￾telligent systems, 2019, 14(3): 551–559. [12] RAFAILID D, CONSTANTINOU E, MANOLO￾POULOS Y. Landmark selection for spectral clustering based on weighted PageRank[J]. Future generation com￾puter systems, 2017, 68: 465–472. [13] LIU Li, SUN Letian, CHEN Shiping, et al. K-PRSCAN: A clustering method based on PageRank[J]. Neurocom￾puting, 2016, 175: 65–80. [14] JIA Hongjie, DING Shifei, DU Mingjing, et al. Approx￾imate normalized cuts without eigen-decomposition[J]. Information sciences, 2016, 374: 135–150. [15] TIAN Fei, GAO Bin, CUI Qing, et al. Learning deep rep￾resentations for graph clustering[C]//Proceedings of the 28th AAAI Conference on Artificial Intelligence. Québec, Canada, 2014: 1293−1299. [16] BANIJAMALI E, GHODSI A. Fast spectral clustering us￾ing autoencoders and landmarks[C]//Proceedings of Inter￾national Conference Image Analysis and Recognition. Montreal, Canada, 2017: 380−388. [17] 光俊叶, 邵伟, 孙亮, 等. 基于融合欧氏距离与 Kendall Tau 距离度量的谱聚类算法 [J]. 控制理论与应用, 2017, 34(6): 783–789. GUANG Junye, SHAO Wei, SUN Liang, et al. Spectral clustering with mixed Euclidean and Kendall Tau metrics[J]. Control theory & applications, 2017, 34(6): 783–789. [18] WEI Kai, TIAN Pingfang, GU Jingguang, et al. RDF data assessment based on metrics and improved PageRank al￾gorithm[C]//Proceedings of International Conference on Database Systems for Advanced Applications. Suzhou, China, 2017: 204−212. [19] 谢娟英, 丁丽娟. 完全自适应的谱聚类算法 [J]. 电子学 报, 2019, 47(5): 1000–1008. XIE Juanying, DING Lijuan. The true self-adaptive spec￾tral clustering algorithms[J]. Acta electronica sinica, 2019, 47(5): 1000–1008. [20] NG A Y, JORDAN M I, WEISS Y. On spectral cluster￾ing: analysis and an algorithm[C]//Proceedings of Neural Information Processing Systems 14, NIPS 2001. Van￾couver, British Columbia, Canada, 2002: 849−856. [21] [22] XIE Juanying, ZHOU Ying, DING Lijuan. Local stand- 第 4 期 张敏，等：结合度量融合和地标表示的自编码谱聚类算法 ·695·