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Methodologies for Cross-Domain Data Fusion: An Overview
Abstract
Traditional data mining usually deals with data from a single domain. In the big data era, we face a diversity of datasets from different sources in different domains. These datasets consist of multiple modalities, each of which has a different representation, distribution, scale, and density. How to unlock the power of knowledge from multiple disparate (but potentially connected) datasets is paramount in big data research, essentially distinguishing big data from traditional data mining tasks. This calls for advanced techniques that can fuse knowledge from various datasets organically in a machine learning and data mining task. This paper summarizes the data fusion methodologies, classifying them into three categories: stage-based, feature level-based, and semantic meaning-based data fusion methods. The last category of data fusion methods is further divided into four groups: multi-view learning-based, similarity-based, probabilistic dependency-based, and transfer learning-based methods. These methods focus on knowledge fusion rather than schema mapping and data merging, significantly distinguishing between cross-domain data fusion and traditional data fusion studied in the database community. This paper does not only introduce high-level principles of each category of methods, but also give examples in which these techniques are used to handle real big data problems. In addition, this paper positions existing works in a framework, exploring the relationship and difference between different data fusion methods. This paper will help a wide range of communities find a solution for data fusion in big data projects.
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“Smart cities require the integration of multiple large and heterogeneous sources of data, for which ML can be a valuable tool, which includes data matching [89,190], data fusion [885], and ensemble learning [451].”
From Tackling climate change with machine learning (2023) · cites this paper as [885]
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
Investigating causal relations by econometric models and cross-spectral methods
Granger, C. W. J. (1969) · Econometrica · doi:10.2307/1912791 · Cited by 22,989
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION
Lee, D. D., & Seung, H. S. (2000) · Cited by 7,972
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Belkin, M., & Niyogi, P. (2003) · Neural Computation · doi:10.1162/089976603321780317 · Cited by 7,562
The random subspace method for constructing decision forests
Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 7,005
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636
Ho, T. K. (2002) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.1995.598994 · Cited by 6,311
Combining labeled and unlabeled data with co-training
Blum, A. L., & Mitchell, T. M. (1998) · Conference on Learning Theory · doi:10.1145/279943.279962 · Cited by 5,597
ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION
Seung, D., & Lee, L.-W. (2001) · Cited by 4,753
Using collaborative filtering to weave an information tapestry
Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. B. (1992) · Communications of the ACM · doi:10.1145/138859.138867 · Cited by 4,071
Canonical Correlation Analysis: An Overview with Application to Learning Methods
Hardoon, D. R., Szedmák, S., & Shawe-Taylor, J. S. (2004) · Neural Computation · doi:10.1162/0899766042321814 · Cited by 3,231
Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition
Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2012) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/tasl.2011.2134090 · Cited by 3,011
Singular value decomposition and least squares solutions
Golub, G. H., & Reinsch, C. (1970) · Numerische Mathematik · doi:10.1007/bf02163027 · Cited by 2,929 · cited 6×
“1) SVD factorizes an m n matrix X into the production of three matricesX ¼ UPV T , where U is a m m real unitary matrix (a.k.a. left singular vectors), P is an m n rectangular diagonal matrix with non-negative real numbers on the diagonal (a.k. a. singular values); V T is a n n real unitary matrix (a.k.a. left singula…”
MethodInfluentialPattern recognition and machine learning
(2007) · Choice Reviews Online · doi:10.5860/choice.44-5091 · Cited by 2,783
(2011) · Cited by 2,752
Non-negative matrix factorization with sparseness constraintsPreprint
Hoyer, P. O. (2004) · arXiv (Cornell University) · doi:10.5555/1005332.1044709 · Cited by 2,620
Bleiholder, J., & Naumann, F. (2009) · ACM Computing Surveys · doi:10.1145/1456650.1456651 · Cited by 1,874
Zheng, Y. (2015) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2743025 · Cited by 1,667
Self-citationMultiple Kernel Learning Algorithms
Gönen, M., & Alpaydın, E. (2011) · Cited by 1,651
The singular value decomposition: Its computation and some applications
Klema, V. C., & Laub, A. J. (1980) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1980.1102314 · Cited by 1,635
Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2629592 · Cited by 1,514
Self-citationMarkov Random Fields and Their Applications
Kindermann, R. P., & Snell, J. L. (1980) · Contemporary mathematics - American Mathematical Society · doi:10.1090/conm/001 · Cited by 1,292
Relational learning via collective matrix factorization
Singh, A. P., & Gordon, G. J. (2008) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1401890.1401969 · Cited by 1,275
A Survey on Multi-view LearningPreprint
Xu, C., Tao, D., & Xu, C. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1304.5634 · Cited by 1,252
Discovering regions of different functions in a city using human mobility and POIs
Yuan, J., Zheng, Y., & Xie, X. (2012) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339561 · Cited by 1,118
Self-citationMultimodal Learning with Deep Boltzmann Machines
Srivastava, N., & Salakhutdinov, R. (2012) · doi:10.5555/2627435.2697059 · Cited by 1,070
Analyzing the effectiveness and applicability of co-training
Nigam, K., & Ghani, R. (2000) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/354756.354805 · Cited by 1,022
Zheng, Y., Liu, F., & Hsieh, H.-P. (2013) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2488188 · Cited by 937
Self-citationGrounded Compositional Semantics for Finding and Describing Images with Sentences
Socher, R., Karpathy, A., Le, Q. V., Manning, C. D., & Ng, A. Y. (2014) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00177 · Cited by 794
A Model of Inductive Bias Learning
Baxter, J. (2000) · Cited by 730
Location-based and preference-aware recommendation using sparse geo-social networking data
Bao, J., Zheng, Y., & Mokbel, M. F. (2012) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2424321.2424348 · Cited by 678
Self-citationGaussian Process Latent Variable Models for Visualisation of High Dimensional Data
Lawrence, N. D. (2003) · Cited by 672
Collaborative location and activity recommendations with GPS history data
Zheng, V. W., Zheng, Y., Xie, X., & Yang, Q. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772795 · Cited by 665
Self-citationSlope One Predictors for Online Rating-Based Collaborative Filtering
Daniel Lemire, A., & Maclachlan, A. (2005) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972757.43 · Cited by 623
Multimodal Neural Language Models
Kiros, R., Salakhutdinov, R., & Zemel, R. (2014) · Cited by 593
A model of inductive bias learning
Baxter, J. (2000) · Journal of Artificial Intelligence Research · doi:10.1613/jair.731 · Cited by 582
Multi-View Discriminant Analysis
Kan, M., Shan, S., Zhang, H., Lao, S., & Chen, X. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2435740 · Cited by 574
Travel time estimation of a path using sparse trajectories
Wang, Y., Zheng, Y., & Xue, Y. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623656 · Cited by 538
Self-citationZheng, Y., Liu, Y., Yuan, J. Y., & Xie, X. (2011) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2030112.2030126 · Cited by 529
Self-citationKERNEL AND NONLINEAR CANONICAL CORRELATION ANALYSIS
Lai, P., & Fyfe, C. (2000) · International Journal of Neural Systems · doi:10.1142/s012906570000034x · Cited by 522
Ranking-based clustering of heterogeneous information networks with star network schema
Sun, Y., Yu, Y., & Han, J. (2009) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557107 · Cited by 520
Forecasting Fine-Grained Air Quality Based on Big Data
Zheng, Y., Yi, X., Li, M., Li, R., Shan, Z., Chang, E., & Li, T. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2788573 · Cited by 497
Self-citationRecommending friends and locations based on individual location history
Zheng, Y., Zhang, L., Ma, Z., Xie, X., & Ma, W. (2011) · ACM Transactions on the Web · doi:10.1145/1921591.1921596 · Cited by 483
Self-citationMining Heterogeneous Information Networks: Principles and Methodologies
Sun, Y., & Han, J. (2012) · Synthesis lectures on data mining and knowledge discovery · doi:10.2200/s00433ed1v01y201207dmk005 · Cited by 474
Discovering Urban Functional Zones Using Latent Activity Trajectories
Yuan, N. J., Zheng, Y., Xie, X., Wang, Y., Zheng, K., & Xiong, H. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2014.2345405 · Cited by 409
Self-citationMore generality in efficient multiple kernel learning
Varma, M., & Babu, B. R. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553510 · Cited by 400
Learning travel recommendations from user-generated GPS traces
Zheng, Y., & Xie, X. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1889681.1889683 · Cited by 398
Self-citationHeterogeneous Transfer Learning for Image Classification
Zhu, Y., Chen, Y., Lu, Z., Pan, S. J., Xue, G.-R., Yu, Y. H., & Yang, Q. (2011) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v25i1.8090 · Cited by 384
Sun, Y., Han, J., Zhao, P., Yin, Z., Cheng, H., & Wu, T. (2009) · International Conference on Extending Database Technology (EDBT) · doi:10.1145/1516360.1516426 · Cited by 383
Crowd sensing of traffic anomalies based on human mobility and social media
Pan, B., Zheng, Y., Wilkie, D., & Shahabi, C. (2013) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2525314.2525343 · Cited by 377
Self-citationSupport Vector Machine Applications in Computational Biology
Noble, W. S. (2004) · The MIT Press eBooks · doi:10.7551/mitpress/4057.003.0005 · Cited by 356
Discovering spatio-temporal causal interactions in traffic data streams
Liu, W., Zheng, Y., Chawla, S., Yuan, J., & Xie, X. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020571 · Cited by 353
Self-citationCollaborative Filtering Meets Mobile Recommendation: A User-Centered Approach
Zheng, V. W., Cao, B., Zheng, Y., Xie, X., & Yang, Q. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7577 · Cited by 326
Self-citationTopic Models Conditioned on Arbitrary Features with Dirichlet-multinomial RegressionPreprint
Mimno, D., & McCallum, A. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.3278 · Cited by 315
Probabilistic Graphical Models
Koller, D., & Friedman, N. (2009) · Cited by 306
Translated Learning: Transfer Learning across Different Feature Spaces
Dai, W., Chen, Y., Xue, G.-R., Yang, Q., & Yu, Y. H. (2008) · Cited by 271
Inferring gas consumption and pollution emission of vehicles throughout a city
Shang, J., Zheng, Y., Tong, W., Chang, E., & Yu, Y. H. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623653 · Cited by 256
Self-citationCo-Training and Expansion: Towards Bridging Theory and Practice
Balcan, M.-F., Blum, A. L., & Yang, K. (2018) · doi:10.1184/r1/6604544 · Cited by 255
Semi-supervised regression with co-training
Zhou, Z., & Li, M. (2005) · International Joint Conference on Artificial Intelligence · Cited by 243
Diagnosing New York city's noises with ubiquitous data
Zheng, Y., Liu, T., Wang, Y., Zhu, Y., Liu, Y., & Chang, E. (2014) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2632048.2632102 · Cited by 234
Self-citationShaban, A., & Yousefi, S. (2024) · Springer optimization and its applications · doi:10.1007/978-3-031-53092-0_10 · Cited by 206
Brefeld, U., & Scheffer, T. (2004) · doi:10.1145/1015330.1015350 · Cited by 200
Multi-view Discriminant Analysis
Kan, M., Shan, S., Zhang, H., Lao, S., & Chen, X. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33718-5_58 · Cited by 199
Finding similar users using category-based location history
Xiao, X., Zheng, Y., Luo, Q., & Xie, X. (2010) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/1869790.1869857 · Cited by 198
Self-citationInferring Air Quality for Station Location Recommendation Based on Urban Big Data
Hsieh, H.-P., Lin, S.-D., & Zheng, Y. (2015) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783344 · Cited by 190
Self-citationInferring the Root Cause in Road Traffic Anomalies
Chawla, S., Zheng, Y., & Hu, J. (2012) · Proceedings · doi:10.1109/icdm.2012.104 · Cited by 187
Self-citationTowards mobile intelligence: Learning from GPS history data for collaborative recommendation
Zheng, V. W., Zheng, Y., Xie, X., & Yang, Q. (2012) · Artificial Intelligence · doi:10.1016/j.artint.2012.02.002 · Cited by 179
Self-citationHeterogeneous transfer learning for image clustering via the social web
Yang, Q., Chen, Y., Xue, G.-R., Dai, W., & Yu, Y. H. (2009) · doi:10.3115/1687878.1687880 · Cited by 172
Tracking via Robust Multi-task Multi-view Joint Sparse Representation
Hong, Z., Mei, X., Prokhorov, D. V., & Tao, D. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.86 · Cited by 155
Abney, S. P. (2001) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/1073083.1073143 · Cited by 148
Inferring social ties between users with human location history
Xiao, X., Zheng, Y., Luo, Q., & Xie, X. (2012) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-012-0117-z · Cited by 147
Self-citationCollaborative Filtering Using Weighted Majority Prediction Algorithms
Nakamura, A., & Abe, N. (1998) · International Conference on Machine Learning · Cited by 144
A Graph-based Framework for Multi-Task Multi-View Learning
He, J., & Lawrence, R. (2011) · Cited by 124
Discovering and Profiling Overlapping Communities in Location-Based Social Networks
Wang, Z., Zhang, D., Zhou, X., Yang, D., Yu, Z., & Yu, Z. (2013) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2013.2256890 · Cited by 105
Detecting collective anomalies from multiple spatio-temporal datasets across different domains
Zheng, Y., Zhang, H., & Yu, Y. H. (2015) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2820783.2820813 · Cited by 105
Self-citationPredictive Subspace Learning for Multi-view Data: a Large Margin Approach
Chen, N., Zhu, J., & Xing, E. P. (2018) · doi:10.1184/r1/6476258.v1 · Cited by 104
Predictive Subspace Learning for Multi-view Data: a Large Margin Approach
Chen, N., Zhu, J., & P. Xing, E. (2018) · doi:10.1184/r1/6476258 · Cited by 103
Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs
Yang, D., Zhang, D., Yu, Z., & Yu, Z. (2013) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2493432.2493464 · Cited by 101
Sparse Real Estate Ranking with Online User Reviews and Offline Moving Behaviors
Fu, Y., Ge, Y., Zheng, Y., Yao, Z., Liu, Y., Xiong, H., & Yuan, J. (2014) · Proceedings · doi:10.1109/icdm.2014.18 · Cited by 88
Self-citationYuan, N. J., Zhang, F., Lian, D., Zheng, K., Yu, S., & Xie, X. (2013) · Conference on Online Social Networks · doi:10.1145/2512938.2512945 · Cited by 84
Multi-view transfer learning with a large margin approach
Zhang, D., He, J., Liu, Y., Si, L., & Lawrence, R. D. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020593 · Cited by 80
Deep Learning: A TutorialPreprint
Polson, N., & Sokolov, V. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.06251 · Cited by 67
Segmentation of Urban Areas Using Road Networks
Yuan, N. J., Zheng, Y., & Xie, X. (2012) · Cited by 65
Self-citationSensing the Pulse of Urban Refueling Behavior
Zhang, F., Yuan, N. J., Wilkie, D., Zheng, Y., & Xie, X. (2015) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2644828 · Cited by 63
Self-citationMulti-view discriminant transfer learning
Yang, P., & Gao, W. G. (2013) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · Cited by 52
Wang, C., Krafft, P., & Mahadevan, S. (2011) · CRC Press eBooks · doi:10.1201/b11431-6 · Cited by 51
A Cloud-Based Knowledge Discovery System for Monitoring Fine-Grained Air Quality
Zheng, Y., Chen, X., Jin, Q., Chen, Y., Qu, X., Liu, X., Chang, E., Ma, W., Yong, R., & Sun, W. (2014) · Cited by 50
Self-citationMulti-task Multi-view Learning for Heterogeneous Tasks
Jin, X. Y., Zhuang, F., Xiong, H., Du, C., Luo, P., & He, Q. (2014) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2661829.2662054 · Cited by 48
Discriminative feature selection for multi-view cross-domain learning
Fang, Z., & Zhang, Z. M. (2013) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2505515.2505532 · Cited by 22
Research works printed but not linked: 6
- tipping, 2001, Sparse Bayesian learning and the relevance vector machine, J Mach Learn Res, 1, 211
- ho, 0, Random decision forest, Proc 3rd Int Conf Document Anal Recog, 278
- sun, 0, Rankclus: Integrating clustering with ranking for heterogeneous information network analysis, Proceedings of the 11th International Conference on Extending Database Technology Advances in Database Technology, 565
- lecun, 0, Deep learning tutorial, Proc Int Conf Mach Learn Tut
- zheng, 0, U-Air: When urban air quality inference meets big data, Proc 19th SIGKDD Conf Knowl Discovery Data Mining, 1436
- bishop, 2006, Graphical models, Pattern Recognition and Machine Learning, 359
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Self-citationMachine learning with big data: challenges and approaches.
L'Heureux, A., Grolinger, K., Elyamany, H. F., & Capretz, M. A. M. (2017) · IEEE Access · doi:10.1109/access.2017.2696365 · Cited by 1,023
Predicting citywide crowd flows using deep spatio-temporal residual networks
Zhang, J., Zheng, Y., Qi, D., Li, R., Yi, X., & Li, T. (2018) · Artificial Intelligence · doi:10.1016/j.artint.2018.03.002 · Cited by 534
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Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics
Lv, Z., Song, H. H., Val, P. B., Steed, A. J., & Jo, M. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2650204 · Cited by 366
Metadata
- Authors
- Zheng, Y.
- Affiliations
- Microsoft Research Asia (China) (CN)
- Corresponding author
- Zheng, Yu
- ORCID
- Zheng, Yu
- Venue
- IEEE Transactions on Big Data
- Published
- 2015-03-01
- Volume
- 1
- Issue
- 1
- Pages
- 16-34
- ISSN
- 2332-7790 (ISSN-L)
- OpenAlex
- W1892382968
- Cited by
- 454
- References
- 101
- Citations by year
- 2026: 23 · 2025: 44 · 2024: 38 · 2023: 42 · 2022: 46 · 2021: 46 · 2020: 47 · 2019: 51 · 2018: 40 · 2017: 38 · 2016: 36 · 2015: 3
- FWCI
- 48.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Big Data (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Quality and Management
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- knowledge fusion, big data, multi-view learning, transfer learning, feature-level fusion, heterogeneous datasets, schema mapping
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