Cited By
View all- Guan ZZhang LPeng JFan J(2015)Multi-View Concept Learning for Data RepresentationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.244854227:11(3016-3028)Online publication date: 1-Nov-2015
Parallel Field Ranking
Authors: 
Ming Ji, Binbin Lin, Xiaofei He, Deng Cai, 
Jiawei HanAuthors Info & Claims
Ming Ji, Binbin Lin, Xiaofei He, Deng Cai, Jiawei HanAuthors Info & ClaimsArticle No.: 15, Pages 1 - 21
Abstract
Recently, ranking data with respect to the intrinsic geometric structure (manifold ranking) has received considerable attentions, with encouraging performance in many applications in pattern recognition, information retrieval and recommendation systems. Most of the existing manifold ranking methods focus on learning a ranking function that varies smoothly along the data manifold. However, beyond smoothness, a desirable ranking function should vary monotonically along the geodesics of the data manifold, such that the ranking order along the geodesics is preserved. In this article, we aim to learn a ranking function that varies linearly and therefore monotonically along the geodesics of the data manifold. Recent theoretical work shows that the gradient field of a linear function on the manifold has to be a parallel vector field. Therefore, we propose a novel ranking algorithm on the data manifolds, called Parallel Field Ranking. Specifically, we try to learn a ranking function and a vector field simultaneously. We require the vector field to be close to the gradient field of the ranking function, and the vector field to be as parallel as possible. Moreover, we require the value of the ranking function at the query point to be the highest, and then decrease linearly along the manifold. Experimental results on both synthetic data and real data demonstrate the effectiveness of our proposed algorithm.
References
[1]
Shivani Agarwal. 2006. Ranking on graph data. In Proceedings of the International Conference on Machine Learning. 25--32.
[2]
Mikhail Belkin and Partha Niyogi. 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Proceedings of the Conference on Advances in Neural Information Processing Systems. 585--591.
[3]
John Canny. 1986. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 6, 679--698.
[4]
Chih-Chung Chang and Chih-Jen Lin. 2001. LIBSVM: A library for support vector machines. Software at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
[5]
Fan R. K. Chung. 1997. Spectral Graph Theory. Regional Conference Series in Mathematics, Vol. 92, AMS.
[6]
Bin Gao, Tie-Yan Liu, Wei Wei, Taifeng Wang, and Hang Li. 2011. Semi-supervised ranking on very large graphs with rich metadata. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 96--104.
[7]
Gene H. Golub and Charles F. Van Loan. 1996. Matrix Computations 3rd Ed. Johns Hopkins University Press.
[8]
Ziyu Guan, Jiajun Bu, Qiaozhu Mei, Chun Chen, and Can Wang. 2009. Personalized tag recommendation using graph-based ranking on multi-type interrelated objects. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 540--547.
[9]
Jingrui He, Mingjing Li, Hong-Jiang Zhang, Hanghang Tong, and Changshui Zhang. 2004. Manifold-ranking based image retrieval. In Proceedings of the ACM International Conference on Multimedia. New York.
[10]
Jan M. Kleinberg. 1999. Authoritative sources in a hyperlinked environment. J. ACM 46, 5, 604--622.
[11]
Martin Lades, Jan C. Vorbrüggen, Joachim M. Buhmann, Jörg Lange, Christoph von der Malsburg, Rolf P. Würtz, and Wolfgang Konen. 1993. Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Comput. 42, 3, 300--311.
[12]
John Lafferty and Larry Wasserman. 2007. Statistical analysis of semi-supervised regression. In Proceedings of the Conference on Advances in Neural Information Processing Systems. 801--808.
[13]
Ken Lang. 1995. NewsWeeder: Learning to filter netnews. In Proceedings of the International Conference on Machine Learning. 331--339.
[14]
John M. Lee. 2003. Introduction to Smooth Manifolds 2nd Ed. Springer.
[15]
Binbin Lin, Chiyuan Zhang, and Xiaofei He. 2011. Semi-supervised regression via parallel field regularization. In Proceedings of the Conference on Advances in Neural Information Processing Systems. 433--441.
[16]
Dong Liu, Xian-Sheng Hua, Linjun Yang, Meng Wang, and Hong-Jiang Zhang. 2009. Tag ranking. In Proceedings of the International World Wide Web Conference. 351--360.
[17]
Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schtze. 2008. Introduction to Information Retrieval. Cambridge University Press.
[18]
Timo Ojala, Matti Pietikäinen, and David Harwood. 1996. A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29, 1, 51--59.
[19]
Lawrence Page, Sergev Brin, Rafeev Motwani, and Terry Winograd. 1998. The PageRank citation ranking: Bringing order to the web. Tech. rep., Stanford University.
[20]
Peter Petersen. 1998. Riemannian Geometry. Springer.
[21]
Sam Roweis and Lawrence Saul. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290, 5500, 2323--2326.
[22]
Terence Sim, Simon Baker, and Maan Bsat. 2003. The CMU pose, illumination, and expression database. IEEE Trans. Pattern Anal. Mach. Intell. 25, 12, 1615--1618.
[23]
Dacheng Tao, Xiaoou Tang, Xuelong Li, and Yong Rui. 2006. Direct kernel biased discriminant analysis: A new content-based image retrieval relevance feedback algorithm. IEEE Trans. Multimedia 8, 4, 716--727.
[24]
Dacheng Tao, Xuelong Li, Xindong Wu, and Stephen J. Maybank. 2009. Geometric mean for subspace selection. IEEE Trans. Pattern Anal. Mach. Intell. 31, 2, 260--274.
[25]
Joshua Tenenbaum, Vin de Silva, and John Langford. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290, 5500, 2319--2323.
[26]
Simon Tong and Edward Y. Chang. 2001. Support vector machine active learning for image retrieval. In Proceedings of the ACM International Conference on Multimedia. 107--118.
[27]
Xiaojun Wan, Jianwu Yang, and Jianguo Xiao. 2007. Manifold-ranking based topic-focused MultiDocument summarization. In Proceedings of the International Joint Conference on Artificial Intelligence. 2903--2908.
[28]
Bin Xu, Jiajun Bu, Chun Chen, Deng Cai, Xiaofei He, Wei Liu, and Jiebo Luo. 2011. Efficient manifold ranking for image retrieval. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 525--534.
[29]
Xun Yuan, Xian-Sheng Hua, Meng Wang, and Xiuqing Wu. 2006. Manifold-ranking based video concept detection on large database and feature pool. In Proceedings of the ACM International Conference on Multimedia. 623--626.
[30]
Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, and Bernhard Schölkopf. 2003. Ranking on Data Manifolds. In Proceedings of the Conference on Advances in Neural Information Processing Systems.
[31]
Xueyuan Zhou, Mikhail Belkin, and Nathan Srebro. 2011. An iterated graph Laplacian approach for ranking on manifolds. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 877--885.
Index Terms
- Parallel Field Ranking
Recommendations
Parallel field ranking
KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data miningRecently, ranking data with respect to the intrinsic geometric structure (manifold ranking) has received considerable attentions, with encouraging performance in many applications in pattern recognition, information retrieval and recommendation systems. ...
Parallel vector field embedding
We propose a novel local isometry based dimensionality reduction method from the perspective of vector fields, which is called parallel vector field embedding (PFE). We first give a discussion on local isometry and global isometry to show the intrinsic ...
Re-ranking search results using query logs
CIKM '06: Proceedings of the 15th ACM international conference on Information and knowledge managementThis work addresses two common problems in search, frequently occurring with underspecified user queries: the top-ranked results for such queries may not contain documents relevant to the user's search intent, and fresh and relevant pages may not get ...
Comments
Information & Contributors
Information
Published In
Copyright © 2013 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 01 September 2013
Accepted: 01 April 2013
Revised: 01 February 2013
Received: 01 September 2012
Published in TKDD Volume 7, Issue 3
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed
Funding Sources
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 332Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 28 Feb 2025
Other Metrics
Citations
Cited By
View all- Guan ZZhang LPeng JFan J(2015)Multi-View Concept Learning for Data RepresentationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.244854227:11(3016-3028)Online publication date: 1-Nov-2015
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in