{"type":"ScholarlyArticle","title":"Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification","authors":[{"name":"Zhao Kang","slug":"zhao-kang","given":"Zhao","family":"Kang","affiliation":"SMILE Lab , School of Computer Science and Engineering University of Electronic Science and Technology of China , Sichuan 611731 , China"},{"name":"Xiao Lu","slug":"xiao-lu","given":"Xiao","family":"Lu","affiliation":"SMILE Lab , School of Computer Science and Engineering University of Electronic Science and Technology of China , Sichuan 611731 , China"},{"name":"Jinfeng Yi","slug":"jinfeng-yi","given":"Jinfeng","family":"Yi","affiliation":"JD AI Research , Beijing 100101 , China"},{"name":"Zenglin Xu","slug":"zenglin-xu","given":"Zenglin","family":"Xu","affiliation":"SMILE Lab , School of Computer Science and Engineering University of Electronic Science and Technology of China , Sichuan 611731 , China"}],"abstract":"Multiple kernel learning (MKL) method is generally believed to perform better than single kernel method. However, some empirical studies show that this is not always true: the combination of multiple kernels may even yield an even worse performance than using a single kernel. There are two possible reasons for the failure: (i) most existing MKL methods assume that the optimal kernel is a linear combination of base kernels, which may not hold true; and (ii) some kernel weights are inappropriately assigned due to noises and carelessly designed algorithms. In this paper, we propose a novel MKL framework by following two intuitive assumptions: (i) each kernel is a perturbation of the consensus kernel; and (ii) the kernel that is close to the consensus kernel should be assigned a large weight. Impressively, the proposed method can automatically assign an appropriate weight to each kernel without introducing additional parameters, as existing methods do. The proposed framework is integrated into a unified framework for graph-based clustering and semi-supervised classification. We have conducted experiments on multiple benchmark datasets and our empirical results verify the superiority of the proposed framework.","doi":"10.24963/ijcai.2018/320","url":"https://ijcai.org/proceedings/2018/320","pdf":"https://ijcai.org/proceedings/2018/0320.pdf","markdown":"https://ijcai.org/proceedings/2018/320.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/320","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":320,"pages":{"first":2312,"last":2318},"keywords":["Machine Learning: Semi-Supervised Learning","Machine Learning: Clustering","Machine Learning: Kernel Methods"],"videos":[],"references":[{"position":1,"raw":"[Cortes, 2009] Corinna Cortes. Can learning kernels help performance. In Invited talk at International Conference on Machine Learning (ICML 2009). Montréal, Canada, 2009.","title":"Can learning kernels help performance","authors":"Corinna Cortes","year":"2009","venue":"Invited talk at International Conference on Machine Learning (ICML 2009)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Du et al., 2015] Liang Du, Peng Zhou, Lei Shi, Hanmo Wang, Mingyu Fan, Wenjian Wang, and Yi-Dong Shen. Robust multiple kernel k-means using l21-norm. In Pro- ceedings of the 24th International Conference on Artificial Intelligence, pages 3476-3482. AAAI Press, 2015.","title":"Robust multiple kernel k-means using l21-norm","authors":"Liang Du, Peng Zhou, Lei Shi, Hanmo Wang, Mingyu Fan, Wenjian Wang, Yi-Dong Shen","year":"2015","venue":"Proceedings of the 24th International Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2015/489"},{"position":3,"raw":"[Fan, 1949] Ky Fan. On a theorem of weyl concerning eigen- values of linear transformations i. Proceedings of the Na- tional Academy of Sciences, 35(11):652-655, 1949.","title":"On a theorem of weyl concerning eigenvalues of linear transformations i","authors":"Fan Ky","year":"1949","venue":"Proceedings of the National Academy of Sciences","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Gehler and Nowozin, 2009] Peter Gehler and Sebastian Nowozin. On feature combination for multiclass object classification. In Computer Vision, 2009 IEEE 12th Inter- national Conference on, pages 221-228. IEEE, 2009.","title":"On feature combination for multiclass object classification","authors":"Nowozin Gehler, Peter Gehler, Sebastian Nowozin","year":"2009","venue":"IEEE 12th International Conference on","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gönen and Alpaydin, 2008] Mehmet Gönen and Ethem Al- paydin. Localized multiple kernel learning. In Pro- ceedings of the 25th international conference on Machine learning, pages 352-359. ACM, 2008.","title":"Localized multiple kernel learning","authors":"Alpaydin Gönen, Mehmet Gönen, Ethem Alpaydin","year":"2008","venue":"Proceedings of the 25th international conference on Machine learning","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hofmann et al., 2008] Thomas Hofmann, Bernhard Schölkopf, and Alexander J Smola. Kernel methods in machine learning. The annals of statistics, pages 1171-1220, 2008.","title":"Kernel methods in machine learning","authors":"Thomas Hofmann, Bernhard Schölkopf, Alexander J Smola","year":"2008","venue":"The annals of statistics","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Huang et al., 2012a] Hsin-Chien Huang, Yung-Yu Chuang, and Chu-Song Chen. Affinity aggregation for spectral clustering. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 773-780. IEEE, 2012.","title":"Affinity aggregation for spectral clustering","authors":"Huang","year":"2012","venue":"Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Huang et al., 2012b] Hsin-Chien Huang, Yung-Yu Chuang, and Chu-Song Chen. Multiple kernel fuzzy cluster- ing. IEEE Transactions on Fuzzy Systems, 20(1):120-134, 2012.","title":"Multiple kernel fuzzy clustering","authors":"Huang","year":"2012","venue":"IEEE Transactions on Fuzzy Systems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Huang et al., 2015] Jin Huang, Feiping Nie, and Heng Huang. A new simplex sparse learning model to measure data similarity for clustering. In IJCAI, pages 3569-3575, 2015.","title":"A new simplex sparse learning model to measure data similarity for clustering","authors":"Jin Huang, Feiping Nie, Heng Huang","year":"2015","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2015/502"},{"position":10,"raw":"[Huang et al., 2018a] Shudong Huang, Zhao Kang, and Zenglin Xu. Self-weighted multi-view clustering with soft capped norm. Knowledge-Based Systems, 2018.","title":"Self-weighted multi-view clustering with soft capped norm","authors":"Huang","year":"2018","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Huang et al., 2018b] Shudong Huang, Zenglin Xu, and Jiancheng Lv. Adaptive local structure learning for doc- ument co-clustering. Knowledge-Based Systems, 148:74- 84, 2018.","title":"Adaptive local structure learning for document co-clustering","authors":"Huang","year":"2018","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kang et al., 2017a] Zhao Kang, Chong Peng, and Qiang Cheng. Kernel-driven similarity learning. Neurocomput- ing, 267:210-219, 2017.","title":"Kernel-driven similarity learning","authors":"Kang","year":"2017","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kang et al., 2017b] Zhao Kang, Chong Peng, and Qiang Cheng. Twin learning for similarity and clustering: A unified kernel approach. In Proceedings of the Thirty- First AAAI Conference on Artificial Intelligence (AAAI- 17). AAAI Press, 2017.","title":"Twin learning for similarity and clustering: A unified kernel approach","authors":"Kang","year":"2017","venue":"Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kang et al., 2018] Zhao Kang, Chong Peng, Qiang Cheng, and Zenglin Xu. Unified spectral clustering with optimal graph. In Proceedings of the Thirty-Second AAAI Con- ference on Artificial Intelligence (AAAI-18). AAAI Press, 2018.","title":"Unified spectral clustering with optimal graph","authors":"Zhao Kang, Chong Peng, Qiang Cheng, Zenglin Xu","year":"2018","venue":"Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Li et al., 2015] Chun-Guang Li, Zhouchen Lin, Honggang Zhang, and Jun Guo. Learning semi-supervised represen- tation towards a unified optimization framework for semi- supervised learning. In Proceedings of the IEEE Interna- tional Conference on Computer Vision, pages 2767-2775, 2015.","title":"Learning semi-supervised representation towards a unified optimization framework for semisupervised learning","authors":"Chun-Guang Li, Zhouchen Lin, Honggang Zhang, Jun Guo","year":"2015","venue":"Proceedings of the IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Liu et al., 2009] Jun Liu, Jianhui Chen, Songcan Chen, and Jieping Ye. Learning the optimal neighborhood kernel for classification. In IJCAI, pages 1144-1149, 2009.","title":"Learning the optimal neighborhood kernel for classification","authors":"Jun Liu, Jianhui Chen, Songcan Chen, Jieping Ye","year":"2009","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2009/193"},{"position":17,"raw":"[Liu et al., 2013] Xinwang Liu, Jianping Yin, Lei Wang, Lingqiao Liu, Jun Liu, Chenping Hou, and Jian Zhang. An adaptive approach to learning optimal neighborhood ker- nels. IEEE transactions on cybernetics, 43(1):371-384, 2013.","title":"An adaptive approach to learning optimal neighborhood kernels","authors":"Xinwang Liu, Jianping Yin, Lei Wang, Lingqiao Liu, Jun Liu, Chenping Hou, Jian Zhang","year":"2013","venue":"IEEE transactions on cybernetics","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Liu et al., 2017] Xinwang Liu, Sihang Zhou, Yueqing Wang, Miaomiao Li, Yong Dou, En Zhu, Jianping Yin, and Han Li. Optimal neighborhood kernel clustering with multiple kernels. In AAAI, pages 2266-2272, 2017.","title":"Optimal neighborhood kernel clustering with multiple kernels","authors":"Liu","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Mohar et al., 1991] Bojan Mohar, Y Alavi, G Chartrand, and OR Oellermann. The laplacian spectrum of graphs. Graph theory, combinatorics, and applications, 2(871- 898):12, 1991.","title":"The laplacian spectrum of graphs","authors":"Bojan Mohar, G Alavi, Chartrand, Oellermann","year":"1991","venue":"Graph theory, combinatorics, and applications","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Ng et al., 2002] Andrew Y Ng, Michael I Jordan, Yair Weiss, et al. On spectral clustering: Analysis and an algo- rithm. Advances in neural information processing systems, 2:849-856, 2002.","title":"On spectral clustering: Analysis and an algorithm","authors":"Y Andrew, Michael I Ng, Yair Jordan, Weiss","year":"2002","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Nie et al., 2014] Feiping Nie, Xiaoqian Wang, and Heng Huang. Clustering and projected clustering with adaptive neighbors. In Proceedings of the 20th ACM SIGKDD in- ternational conference on Knowledge discovery and data mining, pages 977-986. ACM, 2014.","title":"Clustering and projected clustering with adaptive neighbors","authors":"Nie","year":"2014","venue":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Nie et al., 2017a] Feiping Nie, Guohao Cai, and Xuelong Li. Multi-view clustering and semi-supervised classifica- tion with adaptive neighbours. In AAAI, pages 2408-2414, 2017.","title":"Multi-view clustering and semi-supervised classification with adaptive neighbours","authors":"Nie","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Nie et al., 2017b] Feiping Nie, Jing Li, and Xuelong Li. Self-weighted multiview clustering with multiple graphs. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pages 2564-2570, 2017.","title":"Self-weighted multiview clustering with multiple graphs","authors":"Nie","year":"2017","venue":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2017/357"},{"position":24,"raw":"[Peng et al., 2016] Xi Peng, Shijie Xiao, Jiashi Feng, Wei- Yun Yau, and Zhang Yi. Deep subspace clustering with sparsity prior. In IJCAI, pages 1925-1931, 2016.","title":"Deep subspace clustering with sparsity prior","authors":"Xi Peng, Shijie Xiao, Jiashi Feng, Wei-Yun Yau, Zhang Yi","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/275"},{"position":25,"raw":"[Xu et al., 2009] Zenglin Xu, Rong Jin, Irwin King, and Michael Lyu. An extended level method for efficient mul- tiple kernel learning. In Advances in neural information processing systems, pages 1825-1832, 2009.","title":"An extended level method for efficient multiple kernel learning","authors":"Xu","year":"2009","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Xu et al., 2010] Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, and Michael R Lyu. Simple and efficient multiple kernel learning by group lasso. In Proceedings of the 27th international conference on machine learning (ICML-10), pages 1175-1182. Citeseer, 2010.","title":"Simple and efficient multiple kernel learning by group lasso","authors":"Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, Michael R Lyu","year":"2010","venue":"Proceedings of the 27th international conference on machine learning (ICML-10)","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Yang et al., 2017] Yang Yang, Fumin Shen, Zi Huang, , Heng Tao Shen, and Xuelong Li. Discrete nonnegative spectral clustering. IEEE Transactions on Knowledge and Data Engineering, 29(9):1834-1845, 2017.","title":"Discrete nonnegative spectral clustering","authors":"Yang Yang, Fumin Shen, Zi Huang, Heng Tao Shen, Xuelong Li","year":"2017","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Yu et al., 2010] Shi Yu, Tillmann Falck, Anneleen Dae- men, Leon-Charles Tranchevent, Johan AK Suykens, Bart De Moor, and Yves Moreau. L 2-norm multiple ker- nel learning and its application to biomedical data fusion. BMC bioinformatics, 11(1):309, 2010.","title":"L 2-norm multiple kernel learning and its application to biomedical data fusion","authors":"Shi Yu, Tillmann Falck, Anneleen Daemen, Leon-Charles Tranchevent, Johan Ak Suykens, Bart De Moor, Yves Moreau","year":"2010","venue":"BMC bioinformatics","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Zhou et al., 2004] Denny Zhou, Olivier Bousquet, Thomas N Lal, Jason Weston, and Bernhard Schölkopf. Learning with local and global consistency. In Advances in neural information processing systems, pages 321-328, 2004.","title":"Learning with local and global consistency","authors":"Denny Zhou, Olivier Bousquet, Jason Thomas N Lal, Bernhard Weston, Schölkopf","year":"2004","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Zhu et al., 2003] Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In Proceedings of the 20th International conference on Machine learning (ICML-03), pages 912-919, 2003.","title":"Semi-supervised learning using gaussian fields and harmonic functions","authors":"Xiaojin Zhu, Zoubin Ghahramani, John D Lafferty","year":"2003","venue":"Proceedings of the 20th International conference on Machine learning (ICML-03)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}