Transfer knowledge between cities

Ying Wei; Yu Zheng; Qiang Yang

doi:10.1145/2939672.2939830

Transfer knowledge between cities

Ying Wei
, Yu Zheng
, Qiang Yang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

125 Citations (Scopus)

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.

Original language	English
Title of host publication	KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	1905-1914
Volume	13-17-August-2016
ISBN (Print)	9781450342322
DOIs	https://doi.org/10.1145/2939672.2939830
Publication status	Published - 13 Aug 2016
Externally published	Yes
Event	22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 - San Francisco, United States Duration: 13 Aug 2016 → 17 Aug 2016

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume	13-17-August-2016

Conference

Conference	22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
Place	United States
City	San Francisco
Period	13/08/16 → 17/08/16

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Research Keywords

Multi-modality
Transfer learning
Urban computing

Access Output

10.1145/2939672.2939830

Other Access Options

Check CityUHK Library

Permanent Link

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Cite this

Wei, Y., Zheng, Y., & Yang, Q. (2016). Transfer knowledge between cities. In KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol. 13-17-August-2016, pp. 1905-1914). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 13-17-August-2016). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939830

@inproceedings{c75bc77aa38a4c4cac95d8d8d38a33c5,

title = "Transfer knowledge between cities",

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.",

keywords = "Multi-modality, Transfer learning, Urban computing",

author = "Ying Wei and Yu Zheng and Qiang Yang",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected]. ; 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 ; Conference date: 13-08-2016 Through 17-08-2016",

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doi = "10.1145/2939672.2939830",

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volume = "13-17-August-2016",

series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

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booktitle = "KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

address = "United States",

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Wei, Y, Zheng, Y & Yang, Q 2016, Transfer knowledge between cities. in KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. vol. 13-17-August-2016, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 13-17-August-2016, Association for Computing Machinery, pp. 1905-1914, 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, San Francisco, United States, 13/08/16. https://doi.org/10.1145/2939672.2939830

Transfer knowledge between cities. / Wei, Ying; Zheng, Yu; Yang, Qiang.
KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 13-17-August-2016 Association for Computing Machinery, 2016. p. 1905-1914 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 13-17-August-2016).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Transfer knowledge between cities

AU - Wei, Ying

AU - Zheng, Yu

AU - Yang, Qiang

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2016/8/13

Y1 - 2016/8/13

N2 - 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.

AB - 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.

KW - Multi-modality

KW - Transfer learning

KW - Urban computing

UR - https://www.scopus.com/pages/publications/84984985966

UR - https://www.scopus.com/pages/publications/84984985966#tab=impact

U2 - 10.1145/2939672.2939830

DO - 10.1145/2939672.2939830

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781450342322

VL - 13-17-August-2016

T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

SP - 1905

EP - 1914

BT - KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

PB - Association for Computing Machinery

T2 - 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016

Y2 - 13 August 2016 through 17 August 2016

ER -

Wei Y, Zheng Y, Yang Q. Transfer knowledge between cities. In KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 13-17-August-2016. Association for Computing Machinery. 2016. p. 1905-1914. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/2939672.2939830

Transfer knowledge between cities

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Research Keywords

Access Output

Other Access Options

Permanent Link

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Cite this