skip to main content
10.1145/2939672.2939830acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article

Transfer Knowledge between Cities

Published: 13 August 2016 Publication History

Abstract

The rapid urbanization has motivated extensive research on urban computing. It is critical for urban computing tasks to unlock the power of the diversity of data modalities generated by different sources in urban spaces, such as vehicles and humans. However, we are more likely to encounter the label scarcity problem and the data insufficiency problem when solving an urban computing task in a city where services and infrastructures are not ready or just built. In this paper, we propose a FLexible multimOdal tRAnsfer Learning (FLORAL) method to transfer knowledge from a city where there exist sufficient multimodal data and labels, to this kind of cities to fully alleviate the two problems. FLORAL learns semantically related dictionaries for multiple modalities from a source domain, and simultaneously transfers the dictionaries and labelled instances from the source into a target domain. We evaluate the proposed method with a case study of air quality prediction.

References

[1]
J. L. Bentley. Multidimensional binary search trees used for associative searching. Communications of the ACM, 18(9):509--517, 1975.
[2]
J. Blitzer, S. Kakade, and D. P. Foster. Domain adaptation with coupled subspaces. In AISTATS, pages 173--181, 2011.
[3]
T. M. Cover and J. A. Thomas. Elements of information theory. 2012.
[4]
W. Dai, Q. Yang, G.-R. Xue, and Y. Yu. Boosting for transfer learning. In ICML, pages 193--200, 2007.
[5]
Z. Fang and Z. M. Zhang. Discriminative feature selection for multi-view cross-domain learning. In CIKM, pages 1321--1330, 2013.
[6]
D. R. Hardoon, S. Szedmak, and J. Shawe-Taylor. Canonical correlation analysis: An overview with application to learning methods. Neural computation, 16(12):2639--2664, 2004.
[7]
J. He and R. Lawrence. A graph-based framework for multi-task multi-view learning. In ICML, pages 25--32, 2011.
[8]
Z. Jiang, G. Zhang, and L. S. Davis. Submodular dictionary learning for sparse coding. In CVPR, pages 3418--3425, 2012.
[9]
X. Jin, F. Zhuang, H. Xiong, C. Du, P. Luo, and Q. He. Multi-task multi-view learning for heterogeneous tasks. In CIKM, pages 441--450, 2014.
[10]
R. Kiros, R. Salakhutdinov, and R. Zemel. Multimodal neural language models. In ICML, pages 595--603, 2014.
[11]
M.-Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa. Entropy-rate clustering: Cluster analysis via maximizing a submodular function subject to a matroid constraint. PAMI, 36(1):99--112, 2014.
[12]
S. T. McCormick. Submodular function minimization. Handbooks in operations research and management science, 12:321--391, 2005.
[13]
G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher. An analysis of approximations for maximizing submodular set functions. Mathematical Programming, 14(1):265--294, 1978.
[14]
L. Nie, L. Zhang, Y. Yang, M. Wang, R. Hong, and T.-S. Chua. Beyond doctors: future health prediction from multimedia and multimodal observations. In MM, pages 591--600, 2015.
[15]
K. Ozaki, M. Shimbo, M. Komachi, and Y. Matsumoto. Using the mutual k-nearest neighbor graphs for semi-supervised classification of natural language data. In CoNLL, pages 154--162, 2011.
[16]
S. J. Pan and Q. Yang. A survey on transfer learning. Knowledge and Data Engineering, IEEE Transactions on, 22(10):1345--1359, 2010.
[17]
X. Shi, Q. Liu, W. Fan, and P. S. Yu. Transfer across completely different feature spaces via spectral embedding. Knowledge and Data Engineering, IEEE Transactions on, 25(4):906--918, 2013.
[18]
X. Song, L. Nie, L. Zhang, M. Liu, and T.-S. Chua. Interest inference via structure-constrained multi-source multi-task learning. In IJCAI, pages 2371--2377, 2015.
[19]
N. Srivastava and R. R. Salakhutdinov. Multimodal learning with deep boltzmann machines. In NIPS, pages 2222--2230, 2012.
[20]
B. Tan, E. Zhong, E. W. Xiang, and Q. Yang. Multi-transfer: Transfer learning with multiple views and multiple sources. In SDM, 2013.
[21]
Y. Wei, Y. Song, Y. Zhen, B. Liu, and Q. Yang. Scalable heterogeneous translated hashing. In SIGKDD, pages 791--800, 2014.
[22]
Y. Wei, Y. Zhu, C. W.-k. Leung, Y. Song, and Q. Yang. Instilling social to physical: Co-regularized heterogeneous transfer learning. In AAAI, 2016.
[23]
D. Yang, D. Zhang, Z. Yu, and Z. Yu. Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs. In UbiComp, pages 479--488, 2013.
[24]
H. Yang and J. He. Learning with dual heterogeneity: a nonparametric bayes model. In SIGKDD, pages 582--590, 2014.
[25]
P. Yang and W. Gao. Multi-view discriminant transfer learning. In IJCAI, pages 1848--1854, 2013.
[26]
P. Yang, W. Gao, Q. Tan, and K.-F. Wong. Information-theoretic multi-view domain adaptation. In ACL, pages 270--274, 2012.
[27]
P. Yang and J. He. Model Multiple Heterogeneity via Hierarchical Multi-Latent Space Learning. In SIGKDD, pages 1375--1384, 2015.
[28]
Q. Yang, Y. Chen, G.-R. Xue, W. Dai, and Y. Yu. Heterogeneous transfer learning for image clustering via the social web. In ACL, pages 1--9, 2009.
[29]
Z. Yu, F. Wu, Y. Yang, Q. Tian, J. Luo, and Y. Zhuang. Discriminative coupled dictionary hashing for fast cross-media retrieval. In SIGIR, pages 395--404, 2014.
[30]
D. Zhang, J. He, Y. Liu, L. Si, and R. Lawrence. Multi-view transfer learning with a large margin approach. In SIGKDD, pages 1208--1216, 2011.
[31]
J. Zhang and J. Huan. Inductive multi-task learning with multiple view data. In SIGKDD, pages 543--551, 2012.
[32]
Z. Y. Zhao, M. Xie, and M. West. Dynamic dependence networks: Financial time series forecasting and portfolio decisions. Applied Stochastic Models in Business and Industry, 2016.
[33]
Y. Zheng, L. Capra, O. Wolfson, and H. Yang. Urban computing: concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 5(3):38, 2014.
[34]
Y. Zheng, F. Liu, and H.-P. Hsieh. U-air: When urban air quality inference meets big data. In SIGKDD, pages 1436--1444, 2013.
[35]
Y. Zheng, T. Liu, Y. Wang, Y. Zhu, Y. Liu, and E. Chang. Diagnosing new york city's noises with ubiquitous data. In UbiComp, pages 715--725, 2014.
[36]
Y. Zheng, X. Yi, M. Li, R. Li, Z. Shan, E. Chang, and T. Li. Forecasting Fine-Grained Air Quality Based on Big Data. In SIGKDD, pages 2267--2276, 2015.

Cited By

View all
  • (2025)Enhancing PM2.5 prediction by mitigating annual data drift using wrapped loss and neural networksPLOS ONE10.1371/journal.pone.031432720:2(e0314327)Online publication date: 11-Feb-2025
  • (2025)CSSKL: Collaborative Specific-Shared Knowledge Learning framework for cross-city spatiotemporal forecasting in cellular networksInternational Journal of Geographical Information Science10.1080/13658816.2025.2451306(1-38)Online publication date: 16-Jan-2025
  • (2025)AI product cards: a framework for code-bound formal documentation cards in the public administrationData & Policy10.1017/dap.2024.557Online publication date: 8-Jan-2025
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
August 2016
2176 pages
ISBN:9781450342322
DOI:10.1145/2939672
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 August 2016

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. multi-modality
  2. transfer learning
  3. urban computing

Qualifiers

  • Research-article

Funding Sources

  • China National 973 project
  • Hong Kong CERG project

Conference

KDD '16
Sponsor:

Acceptance Rates

KDD '16 Paper Acceptance Rate 66 of 1,115 submissions, 6%;
Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)87
  • Downloads (Last 6 weeks)13
Reflects downloads up to 08 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2025)Enhancing PM2.5 prediction by mitigating annual data drift using wrapped loss and neural networksPLOS ONE10.1371/journal.pone.031432720:2(e0314327)Online publication date: 11-Feb-2025
  • (2025)CSSKL: Collaborative Specific-Shared Knowledge Learning framework for cross-city spatiotemporal forecasting in cellular networksInternational Journal of Geographical Information Science10.1080/13658816.2025.2451306(1-38)Online publication date: 16-Jan-2025
  • (2025)AI product cards: a framework for code-bound formal documentation cards in the public administrationData & Policy10.1017/dap.2024.557Online publication date: 8-Jan-2025
  • (2024)CrossBag: A Bag of Tricks for Cross-City Mobility PredictionProceedings of the 2nd ACM SIGSPATIAL International Workshop on Human Mobility Prediction Challenge10.1145/3681771.3699935(55-59)Online publication date: 29-Oct-2024
  • (2024)A Survey on Knowledge Graph Related Research in Smart City DomainACM Transactions on Knowledge Discovery from Data10.1145/367261518:9(1-31)Online publication date: 19-Jul-2024
  • (2024)One Size Fits All: A Unified Traffic Predictor for Capturing the Essential Spatial–Temporal DependencyIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.325904535:8(11317-11331)Online publication date: Aug-2024
  • (2024)CityTrans: Domain-Adversarial Training With Knowledge Transfer for Spatio-Temporal Prediction Across CitiesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.328352036:1(62-76)Online publication date: Jan-2024
  • (2024)Causality-Guided Stepwise Intervention and Reweighting for Remote Sensing Image Semantic SegmentationIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.343239762(1-17)Online publication date: 2024
  • (2024)Urban Well-being: Leveraging Multi source Data for Informed Decision-Making2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)10.1109/PerComWorkshops59983.2024.10502715(358-359)Online publication date: 11-Mar-2024
  • (2024)Frequency Enhanced Pre-training for Cross-City Few-shot Traffic ForecastingMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70344-7_3(35-52)Online publication date: 22-Aug-2024
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media