{"type":"ScholarlyArticle","title":"Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract)","authors":[{"name":"Ye Zhu","slug":"ye-zhu","given":"Ye","family":"Zhu","affiliation":"School of Information Technology , Deakin University , Geelong , Australia"},{"name":"Kai Ming Ting","slug":"kai-ming-ting","given":"Kai Ming","family":"Ting","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Jiangsu , China"}],"abstract":"This paper presents a new insight into improving the performance of Stochastic Neighbour Embedding (t-SNE) by using Isolation kernel instead of Gaussian kernel. We show that Isolation kernel addresses two deficiencies of t-SNE that employs Gaussian kernel, and the use of Isolation kernel enables t-SNE to deal with large-scale datasets in less runtime without trading off accuracy, unlike existing methods used in speeding up t-SNE.","doi":"10.24963/ijcai.2022/812","url":"https://ijcai.org/proceedings/2022/812","pdf":"https://ijcai.org/proceedings/2022/0812.pdf","markdown":"https://ijcai.org/proceedings/2022/812.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/812","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Journal Track","track":"Journal Track","number":812,"pages":{"first":5792,"last":5796},"keywords":["Machine Learning: Feature Extraction, Selection and Dimensionality Reduction","Data Mining: Data Visualisation"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987235","length_sec":85,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985378","length_sec":429,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Arora et al., 2018] Sanjeev Arora, Wei Hu, and Pravesh K Kothari. An analysis of the t-SNE algorithm for data visu- alization. arXiv preprint arXiv:1803.01768, 2018.","title":"An analysis of the t-SNE algorithm for data visualization","authors":"Sanjeev Arora, Wei Hu, Pravesh K Kothari","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Belkina et al., 2019] Anna C Belkina, Christopher O Cic- colella, Rina Anno, Richard Halpert, Josef Spidlen, and Jennifer E Snyder-Cappione. Automated optimized pa- rameters for t-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nature communications, 10(1):1-12, 2019.","title":"Automated optimized parameters for t-distributed stochastic neighbor embedding improve visualization and analysis of large datasets","authors":"Anna C Belkina, Christopher O Ciccolella, Rina Anno, Richard Halpert, Josef Spidlen, Jennifer E Snyder-Cappione","year":"2019","venue":"Nature communications","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cook et al., 2007] James Cook, Ilya Sutskever, Andriy Mnih, and Geoffrey Hinton. Visualizing similarity data with a mixture of maps. In Artificial Intelligence and Statistics, pages 67-74, 2007.","title":"Visualizing similarity data with a mixture of maps","authors":"James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey Hinton","year":"2007","venue":"Artificial Intelligence and Statistics","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Hinton and Roweis, 2003] Geoffrey E Hinton and Sam T Roweis. Stochastic neighbor embedding. In Advances in neural information processing systems, pages 857-864, 2003.","title":"Stochastic neighbor embedding","authors":"Geoffrey E Hinton, Sam T Roweis","year":"2003","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Lee et al., 2013] John A Lee, Emilie Renard, Guillaume Bernard, Pierre Dupont, and Michel Verleysen. Type 1 and 2 mixtures of kullback-leibler divergences as cost functions in dimensionality reduction based on similarity preserva- tion. Neurocomputing, 112:92-108, 2013.","title":"Type 1 and 2 mixtures of kullback-leibler divergences as cost functions in dimensionality reduction based on similarity preservation","authors":"Emilie John A Lee, Guillaume Renard, Pierre Bernard, Michel Dupont, Verleysen","year":"2013","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Linderman and Steinerberger, 2017] George C Linderman and Stefan Steinerberger. Clustering with t-SNE, provably. arXiv preprint arXiv:1706.02582, 2017.","title":"Clustering with t-SNE, provably","authors":"C George, Stefan Linderman, Steinerberger","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Linderman et al., 2019] George C Linderman, Manas Rachh, Jeremy G Hoskins, Stefan Steinerberger, and Yuval Kluger. Fast interpolation-based t-sne for improved visualization of single-cell rna-seq data. Nature methods, 16(3):243-245, 2019.","title":"Fast interpolation-based t-sne for improved visualization of single-cell rna-seq data","authors":"Manas George C Linderman, Jeremy G Rachh, Stefan Hoskins, Yuval Steinerberger, Kluger","year":"2019","venue":"Nature methods","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Liu et al., 2008] Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. Isolation forest. In Proceedings of the IEEE In- ternational Conference on Data Mining, pages 413-422, 2008.","title":"Isolation forest","authors":"Tony Fei, Kai Liu, Ming Ting, Zhi-Hua Zhou","year":"2008","venue":"Proceedings of the IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Maaten and Hinton, 2008] Laurens van der Maaten and Ge- offrey Hinton. Visualizing data using t-SNE. Journal of machine learning research, 9(Nov):2579-2605, 2008.","title":"Visualizing data using t-SNE","authors":"Maaten, Geoffrey Hinton ; Van Der Maaten, Hinton","year":"2008","venue":"Journal of machine learning research","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Qin et al., 2019] Xiaoyu Qin, Kai Ming Ting, Ye Zhu, and CS Vincent Lee. Nearest-neighbour-induced isolation simi- larity and its impact on density-based clustering. In Thirty- third AAAI Conference on Artificial Intelligence, 2019.","title":"Nearest-neighbour-induced isolation similarity and its impact on density-based clustering","authors":"Xiaoyu Qin, Kai, Ming Ting, Ye Zhu, Lee Vincent","year":"2019","venue":"Thirtythird AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Shaham and Steinerberger, 2017] Uri Shaham and Stefan Steinerberger. Stochastic neighbor embedding separates well-separated clusters. arXiv preprint arXiv:1702.02670, 2017.","title":"Stochastic neighbor embedding separates well-separated clusters","authors":"Steinerberger Shaham, Uri Shaham, Stefan Steinerberger","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Sugiyama and Borgwardt, 2013] Mahito Sugiyama and Karsten Borgwardt. Rapid distance-based outlier detection via sampling. Advances in Neural Information Processing Systems 26, pages 467-475, 2013.","title":"Rapid distance-based outlier detection via sampling","authors":"Borgwardt Sugiyama, Mahito Sugiyama, Karsten Borgwardt","year":"2013","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ting et al., 2017] Kai Ming Ting, Takashi Washio, Jonathan R. Wells, and Sunil Aryal. Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors. Machine Learning, 106:55-91, 2017.","title":"Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors","authors":"Ming Kai, Takashi Ting, Jonathan R Washio, Sunil Wells, Aryal","year":"2017","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Ting et al., 2018] Kai Ming Ting, Yue Zhu, and Zhi-Hua Zhou. Isolation kernel and its effect on SVM. In Proceed- ings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 2329-2337. ACM, 2018.","title":"Isolation kernel and its effect on SVM","authors":"Ming Kai, Yue Ting, Zhi-Hua Zhu, Zhou","year":"2018","venue":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Van Der Maaten and Weinberger, 2012] Laurens Van Der Maaten and Kilian Weinberger. Stochastic triplet embedding. In 2012 IEEE International Workshop on Machine Learning for Signal Processing, pages 1-6. IEEE, 2012.","title":"Stochastic triplet embedding","authors":"Van Der Maaten, Laurens Weinberger, Der Van, Kilian Maaten, Weinberger","year":"2012","venue":"IEEE International Workshop on Machine Learning for Signal Processing","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Van Der Maaten, 2014] Laurens Van Der Maaten. Acceler- ating t-SNE using tree-based algorithms. The Journal of Machine Learning Research, 15(1):3221-3245, 2014.","title":"Laurens Van Der Maaten. Accelerating t-SNE using tree-based algorithms","authors":"Van Der Maaten","year":"2014","venue":"The Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Venna et al., 2010] Jarkko Venna, Jaakko Peltonen, Kristian Nybo, Helena Aidos, and Samuel Kaski. Information re- trieval perspective to nonlinear dimensionality reduction for data visualization. Journal of Machine Learning Research, 11(Feb):451-490, 2010.","title":"Information retrieval perspective to nonlinear dimensionality reduction for data visualization","authors":"Venna","year":"2010","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Yang et al., 2009] Zhirong Yang, Irwin King, Zenglin Xu, and Erkki Oja. Heavy-tailed symmetric stochastic neighbor embedding. In Advances in neural information processing systems, pages 2169-2177, 2009.","title":"Heavy-tailed symmetric stochastic neighbor embedding","authors":"Zhirong Yang, Irwin King, Zenglin Xu, Erkki Oja","year":"2009","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Zhu and Ting, 2021] Ye Zhu and Kai Ming Ting. Improv- ing the effectiveness and efficiency of stochastic neighbour embedding with isolation kernel. Journal of Artificial Intel- ligence Research, 71:667-695, August 2021.","title":"Improving the effectiveness and efficiency of stochastic neighbour embedding with isolation kernel","authors":"Ting ; ] Ye Zhu, Kai Ming Zhu, Ting","year":"2021","venue":"Journal of Artificial Intelligence Research","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}