{"type":"ScholarlyArticle","title":"Label Embedding Based on Multi-Scale Locality Preservation","authors":[{"name":"Cheng-Lun Peng","slug":"cheng-lun-peng","given":"Cheng-Lun","family":"Peng","affiliation":"MOE Key Laboratory of Computer Network and Information Integration , China; School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China"},{"name":"An Tao","slug":"an-tao","given":"An","family":"Tao","affiliation":"School of Information Science and Engineering , Southeast University , Nanjing 210096 , China"},{"name":"Xin Geng","slug":"xin-geng","given":"Xin","family":"Geng","affiliation":"MOE Key Laboratory of Computer Network and Information Integration , China; School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China"}],"abstract":"Label Distribution Learning (LDL) fits the situations well that focus on the overall distribution of the whole series of labels. The numerical labels of LDL satisfy the integrity probability constraint. Due to LDL's special label domain, existing label embedding algorithms that focus on embedding of binary labels are thus unfit for LDL. This paper proposes a specially designed approach MSLP that achieves label embedding for LDL by Multi-Scale Locality Preserving (MSLP). Specifically, MSLP takes the locality information of data in both the label space and the feature space into account with different locality granularity. By assuming an explicit mapping from the features to the embedded labels, MSLP does not need an additional learning process after completing embedding. Besides, MSLP is insensitive to the existing of data points violating the smoothness assumption, which is usually caused by noises. Experimental results demonstrate the effectiveness of MSLP in preserving the locality structure of label distributions in the embedding space and show its superiority over the state-of-the-art baseline methods.","doi":"10.24963/ijcai.2018/364","url":"https://ijcai.org/proceedings/2018/364","pdf":"https://ijcai.org/proceedings/2018/0364.pdf","markdown":"https://ijcai.org/proceedings/2018/364.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/364","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":364,"pages":{"first":2623,"last":2629},"keywords":["Machine Learning: Multi-instance;Multi-label;Multi-view learning","Machine Learning: Dimensionality Reduction and Manifold Learning","Machine Learning: Machine Learning","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Belkin and Niyogi, 2002] M. Belkin and P. Niyogi. Lapla- cian eigenmaps and spectral techniques for embedding and clustering. In NIPS, pages 585-591, Vancouver, Canada, 2002.","title":"Laplacian eigenmaps and spectral techniques for embedding and clustering","authors":"M Belkin, P Niyogi","year":"2002","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bhatia et al., 2015] K. Bhatia, H. Jain, P. Kar, M. Varma, and P. Jain. Sparse local embeddings for extreme multi- label classification. In NIPS, pages 730-738, Montréal, Canada, 2015.","title":"Sparse local embeddings for extreme multilabel classification","authors":"K Bhatia, H Jain, P Kar, M Varma, P Jain","year":"2015","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Borchani et al., 2015] H. Borchani, G. Varando, C. Bielza, and P. Larrañaga. A survey on multi-output regression. WIREs Data Mining and Knowledge Discovery, 5(5):216- 233, 2015.","title":"A survey on multi-output regression","authors":"H Borchani, G Varando, C Bielza, P Larrañaga","year":"2015","venue":"WIREs Data Mining and Knowledge Discovery","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chapelle et al., 2006] O. Chapelle, B. Scholkopf, and A. Zien. Semi-Supervised Learning. MIT Press, London, U.K., 2006.","title":"Semi-Supervised Learning","authors":"O Chapelle, B Scholkopf, A Zien","year":"2006","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen and Lin, 2012] Y.-N. Chen and H.-T. Lin. Feature- aware label space dimension reduction for multi-label classification. In NIPS, pages 1529-1537, Lake Tahoe, NV, 2012.","title":"Featureaware label space dimension reduction for multi-label classification","authors":"Y.-N Chen, H.-T Lin","year":"2012","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Ferng and Lin, 2013] C.-S. Ferng and Hsuan-T. Lin. Multi- label classification using error-correcting codes of hard or soft bits. TNNLS, 24(11):1888-1900, 2013.","title":"Multilabel classification using error-correcting codes of hard or soft bits","authors":"C.-S Ferng, Hsuan-T Lin","year":"2013","venue":"TNNLS","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Gao et al., 2017] B.-B. Gao, C. Xing, C.-W. Xie, J.-X. Wu, and X. Geng. Deep label distribution learning with label ambiguity. TIP, 26(6):2825-2838, 2017.","title":"Deep label distribution learning with label ambiguity","authors":"B.-B Gao, C Xing, C.-W Xie, J.-X Wu, X Geng","year":"2017","venue":"TIP","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"Geng and Hou, ] X. Geng and P. Hou. Pre-release predic- tion of crowd opinion on movies by label distribution learning. In AAAI, pages 3511-3517, Buenos Aires, Ar- gentina.","title":"Pre-release prediction of crowd opinion on movies by label distribution learning","authors":"Geng, ] X Hou, P Geng, Hou","year":null,"venue":"AAAI","doi":null,"url":null,"internal_url":"/proceedings/2015/494"},{"position":9,"raw":"[Geng and Ling, 2017] X. Geng and M.G. Ling. Soft video parsing by label distribution learning. In AAAI, pages 1331-1337, San Francisco, CA, 2017.","title":"Soft video parsing by label distribution learning","authors":"Ling ; Geng, X Geng, M G Ling","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Geng and Luo, 2014] X. Geng and L.-R. Luo. Multilabel ranking with inconsistent rankers. In CVPR, pages 3742- 3747, Columbus, OH, 2014.","title":"Multilabel ranking with inconsistent rankers","authors":"Luo Geng, X Geng, L.-R Luo","year":"2014","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Geng et al., 2010] X. Geng, K. Smith-Miles, and Z.-H. Zhou. Facial age estimation by learning from label dis- tributions. In AAAI, Atlanta, GA, 2010.","title":"Facial age estimation by learning from label distributions","authors":"X Geng, K Smith-Miles, Z.-H Zhou","year":"2010","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Geng et al., 2013] X. Geng, C. Yin, and Z.-H. Zhou. Fa- cial age estimation by learning from label distributions. TPAMI, 35(10):2401-2412, 2013.","title":"Facial age estimation by learning from label distributions","authors":"X Geng, C Yin, Z.-H Zhou","year":"2013","venue":"TPAMI","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Geng, 2016] X. Geng. Label distribution learning. TKDE, 28(7):1734-1748, 2016.","title":"Label distribution learning","authors":"X Geng","year":"2016","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Hardoon et al., 2003] D. Hardoon, S. Szedmak, and J. Shawe-Taylor. Canonical correlation analysis: An overview with application to learning methods. Neuro- computing, 16(12):2639-2664, 2003.","title":"Canonical correlation analysis: An overview with application to learning methods","authors":"D Hardoon, S Szedmak, J Shawe-Taylor","year":"2003","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[He and Niyogi, 2004] X.-F. He and P. Niyogi. Locality pre- serving projections. In NIPS, pages 153-160, Vancouver, Canada, 2004.","title":"Locality preserving projections","authors":"X.-F He, P Niyogi","year":"2004","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[He et al., 2005] X.-F. He, D. Cai, S.-C. Yan, and H.-J. Zhang. Neighborhood preserving embedding. In ICCV, pages 1208-1213, Beijing, China, 2005.","title":"Neighborhood preserving embedding","authors":"X.-F He, D Cai, S.-C Yan, H.-J Zhang","year":"2005","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Hsu et al., 2009] D. Hsu, S. Kakade, J. Langford, and T. Zhang. Multi-label prediction via compressed sensing. In NIPS, pages 772-780, Lake Tahoe, NV, 2009.","title":"Multi-label prediction via compressed sensing","authors":"D Hsu, S Kakade, J Langford, T Zhang","year":"2009","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Jolliffe, 1986] I. T. Jolliffe. Principal Component Analysis. Springer, 1986.","title":"Principal Component Analysis","authors":"I T Jolliffe","year":"1986","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lin et al., 2014] Z.-J. Lin, G-.G. Ding, M.-Q. Hu, and J.-M. Wang. Multi-label classification via feature-aware implicit label space encoding. In ICML, pages 325-333, Beijing, China, 2014.","title":"Multi-label classification via feature-aware implicit label space encoding","authors":"Z.-J Lin, G - G Ding, M.-Q Hu, J.-M Wang","year":"2014","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Lyons et al., 1998] M. J. Lyons, S. Akamatsu, M. Kamachi, and J. Gyoba. Coding facial expressions with gabor wavelets. In FGR, pages 200-205, Nara, Japan, 1998.","title":"Coding facial expressions with gabor wavelets","authors":"M J Lyons, S Akamatsu, M Kamachi, J Gyoba","year":"1998","venue":"FGR","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"Ren and Geng, ] Y. Ren and X. Geng. Sense beauty by label distribution learning. In IJCAI, Melbourne, Australia.","title":"Sense beauty by label distribution learning","authors":"Ren, ] Y Geng, X Ren, Geng","year":null,"venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/369"},{"position":22,"raw":"[Shen et al., 2017] W. Shen, K. Zhao, Y.-L. Guo, and A. Yuille. Label distribution learning forests. In NIPS, Long Beach, CA, 2017.","title":"Label distribution learning forests","authors":"W Shen, K Zhao, Y.-L Guo, A Yuille","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Tai and Lin, 2012] F. Tai and H.-T. Lin. Multilabel classi- fication with principal label space transformation. Neuro- computing, 24(9):2508-2542, 2012.","title":"Multilabel classification with principal label space transformation","authors":"F Tai, H.-T Lin","year":"2012","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Yeh et al., 2017] C.-K. Yeh, W.-C. Wu, W.-J. Ko, and Y.- C. F. Wang. Learning deep latent space for multi-label classification. In AAAI, pages 2838-2844, San Francisco, CA, 2017.","title":"Learning deep latent space for multi-label classification","authors":"C.-K Yeh, W.-C Wu, W.-J Ko, Y.-C F Wang","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zeng et al., 2017] J. Zeng, Y. Liu, B. Leng, Z. Xiong, and Y. M. Cheung. Dimensionality reduction in multiple ordi- nal regression. TNNLS, (99):1-14, 2017.","title":"Dimensionality reduction in multiple ordinal regression","authors":"J Zeng, Y Liu, B Leng, Z Xiong, Y M Cheung","year":"2017","venue":"TNNLS","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhang and Schneider, 2012] Y. Zhang and J. Schneider. Maximum margin output coding. In ICML, pages 1575- 1582, Edinburgh, UK, 2012.","title":"Maximum margin output coding","authors":"Y Zhang, J Schneider","year":"2012","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zhang and Zhou, 2014] M.-L. Zhang and Z.-H. Zhou. A review on multi-label learning algorithms. TKDE, 26(8):1819-1837, 2014.","title":"A review on multi-label learning algorithms","authors":"M.-L Zhang, Z.-H Zhou","year":"2014","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Zhang et al., 2015] Z.-X. Zhang, M. Wang, and X. Geng. Crowd counting in public video surveillance by label dis- tribution learning. Neurocomputing, pages 151-163, 2015.","title":"Crowd counting in public video surveillance by label distribution learning","authors":"Z.-X Zhang, M Wang, X Geng","year":"2015","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Zhou et al., 2015] Y. Zhou, H. Xue, and X. Geng. Emo- tion distribution recognition from facial expressions. In ACMM, pages 1247-1250, Brisbane, Australia, 2015.","title":"Emotion distribution recognition from facial expressions","authors":"Y Zhou, H Xue, X Geng","year":"2015","venue":"ACMM","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}