Associate Professor Xiaofeng Zhu staff profile picture

Contact details +64 (09) 414 0800  ext. 43649

Associate Professor Xiaofeng Zhu Ph.D

Associate Professor - Big Data/Computer Science

School of Natural and Computational Sciences

Professional

Contact details

  • Location: 3:07, INMS
    Campus: Albany

Qualifications

  • Doctor of Philosophy - University of Queensland (2013)

Research Expertise

Research Interests

Mining useful knowledge or information from big multimedia data and medical imaging data

Thematics

Health and Well-being

Area of Expertise

Field of research codes
Artificial Intelligence and Image Processing (080100): Computer Vision (080104): Information And Computing Sciences (080000): Pattern Recognition and Data Mining (080109)

Research Projects

Summary of Research Projects

Position Current Completed
Project Leader 2 0

Current Projects

Project Title: Pattern Discovery from Big Medical Data

Date Range: 2018 - 2021

Funding Body: Royal Society of New Zealand

Project Team:

Research Outputs

Journal

Zhu, Y., Zhu, X., Kim, M., Yan, J., Kaufer, D., & Wu, G. (2019). Dynamic Hyper-Graph Inference Framework for Computer-Assisted Diagnosis of Neurodegenerative Diseases. IEEE Transactions on Medical Imaging. 38(2), 608-616
[Journal article]Authored by: Zhu, X.
Zhang, Y., Zhang, H., Chen, X., Liu, M., Zhu, X., Lee, SW., . . . Shen, D. (2019). Strength and similarity guided group-level brain functional network construction for MCI diagnosis. Pattern Recognition. 88, 421-430
[Journal article]Authored by: Zhu, X.
Zhu, X., Suk, HI., & Shen, D. (2019). Low-rank dimensionality reduction for multi-modality neurodegenerative disease identification. World Wide Web. 22(2), 907-925
[Journal article]Authored by: Zhu, X.
Zhu, X., Suk, HI., & Shen, D. (2019). Group sparse reduced rank regression for neuroimaging genetic study. World Wide Web. 22(2), 673-688
[Journal article]Authored by: Zhu, X.
Zhu, X., Hu, R., Lei, C., Thung, KH., Zheng, W., & Wang, C. (2019). Low-rank hypergraph feature selection for multi-output regression. World Wide Web. 22(2), 517-531
[Journal article]Authored by: Zhu, X.
Zhu, X., Suk, HI., Lee, SW., & Shen, D. (2019). Discriminative self-representation sparse regression for neuroimaging-based alzheimer’s disease diagnosis. Brain Imaging and Behavior. 13(1), 27-40
[Journal article]Authored by: Zhu, X.
Zhu, X., Zhang, S., Hu, R., He, W., Lei, C., & Zhu, P. (2018). One-step Multi-view Spectral Clustering. IEEE Transactions on Knowledge and Data Engineering.
[Journal article]Authored by: Zhu, X.
Tong, T., Zhu, X., & Du, T. (2018). Connected graph decomposition for spectral clustering. Multimedia Tools and Applications.
[Journal article]Authored by: Zhu, X.
Zhu, X., Sanroma, G., Zhang, J., & Munsell, BC. (2018). Editorial: Deep Mining Big Social Data. World Wide Web. 21(6), 1449-1452
[Journal article]Authored by: Zhu, X.
Wu, L., Zhu, X., & Tong, T. (2018). Global and local clustering with kNN and local PCA. Multimedia Tools and Applications. 77(22), 29727-29738
[Journal article]Authored by: Zhu, X.
Zhu, X., Zhang, S., Li, Y., Zhang, J., Yang, L., & Fang, Y. (2018). Low-rank Sparse Subspace for Spectral Clustering. IEEE Transactions on Knowledge and Data Engineering.
[Journal article]Authored by: Zhu, X.
Zheng, W., Zhu, X., Wen, G., Zhu, Y., Yu, H., & Gan, J. (2018). Unsupervised feature selection by self-paced learning regularization. Pattern Recognition Letters.
[Journal article]Authored by: Zhu, X.
Zheng, W., Zhu, X., Zhu, Y., Hu, R., & Lei, C. (2018). Dynamic graph learning for spectral feature selection. Multimedia Tools and Applications. 77(22), 29739-29755
[Journal article]Authored by: Zhu, X.
Lei, C., & Zhu, X. (2018). Unsupervised feature selection via local structure learning and sparse learning. Multimedia Tools and Applications. 77(22), 29605-29622
[Journal article]Authored by: Zhu, X.
Zhu, X., Shao, J., & Zhang, J. (2018). Pattern discovery from multi-source data. Pattern Recognition Letters. 109, 1-3
[Journal article]Authored by: Zhu, X.
Zhang, S., Li, X., Zong, M., Zhu, X., & Wang, R. (2017). Efficient kNN Classification With Different Numbers of Nearest Neighbors. IEEE Transactions on Neural Networks and Learning Systems.
[Journal article]Authored by: Wang, R., Zhu, X.
Zhu, X., Zhang, S., Hu, R., Zhu, Y., & Song, J. (2017). Local and Global Structure Preservation for Robust Unsupervised Spectral Feature Selection. IEEE Transactions on Knowledge and Data Engineering.
[Journal article]Authored by: Zhu, X.
Song, J., Gao, L., Liu, L., Zhu, X., Sebe, N., & Zhu, . (2017). Quantization-based hashing: a general framework for scalable image and video retrieval. Pattern Recognition.
[Journal article]Authored by: Zhu, X.
Zhu, X., Li, X., Zhang, S., Ju, C., & Wu, X. (2017). Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection. IEEE Transactions on Neural Networks and Learning Systems. 28(6), 1263-1275
[Journal article]Authored by: Zhu, X.
Zhu, X., Jin, Z., & Ji, R. (2017). Learning high-dimensional multimedia data. Multimedia Systems. 23(3), 281-283
[Journal article]Authored by: Zhu, X.
Hu, R., Zhu, X., Cheng, D., He, W., Yan, Y., Song, J., . . . Zhang, S. (2017). Graph self-representation method for unsupervised feature selection. Neurocomputing. 220, 130-137
[Journal article]Authored by: Zhu, X.
He, W., Zhu, X., Cheng, D., Hu, R., & Zhang, S. (2017). Low-rank unsupervised graph feature selection via feature self-representation. Multimedia Tools and Applications. 76(9), 12149-12164
[Journal article]Authored by: Zhu, X.
He, W., Zhu, X., Cheng, D., Hu, R., & Zhang, S. (2017). Unsupervised feature selection for visual classification via feature-representation property. Neurocomputing. 236, 5-13
[Journal article]Authored by: Zhu, X.
Zhang, S., Li, X., Zong, M., Zhu, X., & Cheng, D. (2017). Learning k for kNN Classification. ACM Transactions on Intelligent Systems and Technology. 8(3)
[Journal article]Authored by: Zhu, X.
Zhu, X., Luo, X., & Xu, C. (2017). Editorial learning for multimodal data. Neurocomputing. 253, 1-5
[Journal article]Authored by: Zhu, X.
Zhu, X., Li, X., Zhang, S., Xu, Z., Yu, L., & Wang, C. (2017). Graph PCA Hashing for Similarity Search. IEEE Transactions on Multimedia. 19(9), 2033-2044
[Journal article]Authored by: Zhu, X.
Zhu, X., Suk, HI., Wang, L., Lee, SW., & Shen, D. (2017). A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Medical Image Analysis. 38, 205-214
[Journal article]Authored by: Zhu, X.
Wang, Z., Zhu, X., Adeli, E., Zhu, Y., Nie, F., Munsell, B., . . . Wu, G. (2017). Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning. Medical Image Analysis. 39, 218-230
[Journal article]Authored by: Zhu, X.
Zhu, X., Suk, H-I., Huang, H., & Shen, D. (2017). Low-Rank Graph-Regularized Structured Sparse Regression for Identifying Genetic Biomarkers.. IEEE Trans Big Data. 3(4), 405-414
[Journal article]Authored by: Zhu, X.
Zhu, X., Li, X., & Zhang, S. (2016). Block-Row Sparse Multiview Multilabel Learning for Image Classification. IEEE Transactions on Cybernetics. 46(2), 450-461
[Journal article]Authored by: Zhu, X.
Zhu, X., Lu, F., Xu, C., & Ji, R. (2016). Learning for medical imaging. Neurocomputing. 195, 1-5
[Journal article]Authored by: Zhu, X.
Zhu, X., Suk, HI., Lee, SW., & Shen, D. (2016). Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification. IEEE Transactions on Biomedical Engineering. 63(3), 607-618
[Journal article]Authored by: Zhu, X.
Li, W., Bi, Y., Zhu, X., Yuan, CA., & Zhang, XB. (2016). Hybrid swarm intelligent parallel algorithm research based on multi-core clusters. Microprocessors and Microsystems. 47, 151-160
[Journal article]Authored by: Zhu, X.
Zhu, X., Suk, HI., Lee, SW., & Shen, D. (2016). Canonical feature selection for joint regression and multi-class identification in Alzheimer’s disease diagnosis. Brain Imaging and Behavior. 10(3), 818-828
[Journal article]Authored by: Zhu, X.
Yang, Y., Zha, ZJ., Gao, Y., Zhu, X., & Chua, TS. (2015). Erratum: Exploiting web images for semantic video indexing via robust sample-specific loss (IEEE Transactions on Multimedia (2014) 16:6 (1677-1689)). IEEE Transactions on Multimedia. 17(2), 256
[Journal article]Authored by: Zhu, X.
Zhu, X., Xie, Q., Zhu, Y., Liu, X., & Zhang, S. (2015). Multi-view multi-sparsity kernel reconstruction for multi-class image classification. Neurocomputing. 169, 43-49
[Journal article]Authored by: Zhu, X.
Zhu, X., Zhang, L., & Huang, Z. (2014). A sparse embedding and least variance encoding approach to hashing. IEEE Transactions on Image Processing. 23(9), 3737-3750
[Journal article]Authored by: Zhu, X.
Yang, Y., Zha, ZJ., Gao, Y., Zhu, X., & Chua, TS. (2014). Exploiting web images for semantic video indexing via robust sample-specific loss. IEEE Transactions on Multimedia. 16(6), 1677-1689
[Journal article]Authored by: Zhu, X.
Zhu, X., Suk, HI., & Shen, D. (2014). A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis. NeuroImage. 100, 91-105
[Journal article]Authored by: Zhu, X.
Zhu, X., Huang, Z., Cui, J., & Shen, HT. (2013). Video-to-shot tag propagation by graph sparse group lasso. IEEE Transactions on Multimedia. 15(3), 633-646
[Journal article]Authored by: Zhu, X.
Zhu, X., Huang, Z., Cheng, H., Cui, J., & Shen, HT. (2013). Sparse hashing for fast multimedia search. ACM Transactions on Information Systems. 31(2),
[Journal article]Authored by: Zhu, X.
Zhu, X., Huang, Z., Yang, Y., Shen, HT., Xu, C., & Luo, J. (2013). Self-taught dimensionality reduction on the high-dimensional small-sized data. Pattern Recognition. 46(1), 215-229
[Journal article]Authored by: Zhu, X.
Huang, FL., Zhang, SC., & Zhu, XF. (2013). Discovering network community based on multi-objective optimization. Ruan Jian Xue Bao/Journal of Software. 24(9), 2062-2077
[Journal article]Authored by: Zhu, X.
Zhu, X., Huang, Z., Shen, HT., Cheng, J., & Xu, C. (2012). Dimensionality reduction by mixed kernel canonical correlation analysis. Pattern Recognition. 45(8), 3003-3016
[Journal article]Authored by: Zhu, X.
Qin, Y., Zhang, S., Zhu, X., Zhang, J., & Zhang, C. (2009). Estimating confidence intervals for structural differences between contrast groups with missing data. Expert Systems with Applications. 36(3 PART 2), 6431-6438
[Journal article]Authored by: Zhu, X.
Qin, Y., Zhang, S., Zhu, X., Zhang, J., & Zhang, C. (2009). POP algorithm: Kernel-based imputation to treat missing values in knowledge discovery from databases. Expert Systems with Applications. 36(2 PART 2), 2794-2804
[Journal article]Authored by: Zhu, X.
Zhang, S., Huang, Z., Zhang, J., & Zhu, X. (2008). Mining follow-up correlation patterns from time-related databases. Knowledge and Information Systems. 14(1), 81-100
[Journal article]Authored by: Zhu, X.
Qin, Y., Zhang, S., Zhu, X., Zhang, J., & Zhang, C. (2007). Semi-parametric optimization for missing data imputation. Applied Intelligence. 27(1), 79-88
[Journal article]Authored by: Zhu, X.

Conference

Zhu, Y., Zhu, X., & Zheng, W.Robust multi-view learning via half-quadratic minimization. IJCAI International Joint Conference on Artificial Intelligence. (pp. 3278 - 3284). 1045-0823.
[Conference]Authored by: Zhu, X.
Zheng, W., Zhu, X., Zhu, Y., & Zhang, S.Robust feature selection on incomplete data. IJCAI International Joint Conference on Artificial Intelligence. (pp. 3191 - 3197). 1045-0823.
[Conference]Authored by: Zhu, X.
Zhu, X., Lei, C., Yu, H., Li, Y., Gan, J., & Zhang, S.Robust graph dimensionality reduction. IJCAI International Joint Conference on Artificial Intelligence. (pp. 3257 - 3263). 1045-0823.
[Conference]Authored by: Zhu, X.
Zhu, Y., Zhu, X., Kim, M., Kaufer, D., & Wu, G. (2017). A novel dynamic hyper-graph inference framework for computer assisted diagnosis of neuro-diseases. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10265 LNCS (pp. 158 - 169).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, X., He, W., Li, Y., Yang, Y., Zhang, S., Hu, R., . . . Zhu, Y. (2017). One-step spectral clustering via dynamically learning affinity matrix and subspace. 31st AAAI Conference on Artificial Intelligence, AAAI 2017. (pp. 2963 - 2969).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Wang, Q., Wang, S., Zhu, X., Liu, T., Humphrey, Z., Ghukasyan, V., . . . Wu, G. (2017). Accurate and high throughput cell segmentation method for mouse brain nuclei using cascaded convolutional neural network. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10530 LNCS (pp. 55 - 62).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, Y., Kim, M., Zhu, X., Yan, J., Kaufer, D., & Wu, G. (2017). Personalized diagnosis for Alzheimer’s disease. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10435 LNCS (pp. 205 - 213).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhang, Y., Zhang, H., Chen, X., Liu, M., Zhu, X., & Shen, D. (2017). Inter-subject similarity guided brain network modeling for MCI diagnosis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10541 LNCS (pp. 168 - 175).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, X., Zhu, Y., Zhang, S., Hu, R., & He, W. (2017). Adaptive hypergraph learning for unsupervised feature selection. IJCAI International Joint Conference on Artificial Intelligence. (pp. 3581 - 3587).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhou, T., Thung, KH., Zhu, X., & Shen, D. (2017). Feature learning and fusion of multimodality neuroimaging and genetic data for multi-status dementia diagnosis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10541 LNCS (pp. 132 - 140).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, X., Thung, KH., Adeli, E., Zhang, Y., & Shen, D. (2017). Maximum mean discrepancy based multiple kernel learning for incomplete multimodality neuroimaging data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10435 LNCS (pp. 72 - 80).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhang, S., Yang, L., Li, Y., Luo, Y., & Zhu, X. (2016). Low-rank feature reduction and sample selection for multi-output regression. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10086 LNAI (pp. 126 - 141).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
He, W., Zhu, X., Li, Y., Hu, R., Zhu, Y., & Zhang, S. (2016). Unsupervised hypergraph feature selection with low-rank and self-representation constraints. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10086 LNAI (pp. 172 - 187).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Hu, R., Zhu, X., He, W., Zhang, J., & Zhang, S. (2016). Supervised feature selection by robust sparse reduced-rank regression. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10086 LNAI (pp. 700 - 713).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, X., Thung, KH., Zhang, J., & Shen, D. (2016). Fast neuroimaging-based retrieval for Alzheimer’s disease analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10019 LNCS (pp. 313 - 321).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, X., Suk, HI., Thung, KH., Zhu, Y., Wu, G., & Shen, D. (2016). Joint discriminative and representative feature selection for alzheimer’s disease diagnosis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 10019 LNCS (pp. 77 - 85).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, Y., Zhu, X., Kim, M., Shen, D., & Wu, G. (2016). Early diagnosis of Alzheimer’s disease by joint feature selection and classification on temporally structured support vector machine. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9900 LNCS (pp. 264 - 272).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, Y., Zhu, X., Zhang, H., Gao, W., Shen, D., & Wu, G. (2016). Reveal consistent spatial-temporal patterns from dynamic functional connectivity for autism spectrum disorder identification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9900 LNCS (pp. 106 - 114).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Wang, Z., Zhu, X., Adeli, E., Zhu, Y., Zu, C., Nie, F., . . . Wu, G. (2016). Progressive graph-based transductive learning for multi-modal classification of brain disorder disease. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9900 LNCS (pp. 291 - 299).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, X., Suk, HI., Huang, H., & Shen, D. (2016). Structured sparse low-rank regression model for brain-wide and genome-wide associations. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9900 LNCS (pp. 344 - 352).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Vanderweyen, D., Munsell, BC., Mintzer, JE., Mintzer, O., Gajadhar, A., Zhu, X., . . . Joseph, J. (2015). Identifying abnormal network alterations common to traumatic brain injury and Alzheimer’s disease patients using functional connectome data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9352 (pp. 229 - 237).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Wu, G., Zhu, X., Wang, Q., & Shen, D. (2015). Image super-resolution by supervised adaption of patchwise self-similarity from high-resolution image. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9467 (pp. 10 - 18).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Gao, L., Song, J., Shao, J., Zhu, X., & Shen, HT. (2015). Zero-shot image categorization by image correlation exploration. ICMR 2015 - Proceedings of the 2015 ACM International Conference on Multimedia Retrieval. (pp. 487 - 490).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, X., Suk, HI., Zhu, Y., Thung, KH., Wu, G., & Shen, D. (2015). Multi-view classification for identification of Alzheimer’s disease. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9352 (pp. 255 - 262).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, X., Suk, HI., & Shen, D. (2014). A novel multi-relation regularization method for regression and classification in AD diagnosis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8675 LNCS (pp. 401 - 408).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, X., Suk, HI., & Shen, D. (2014). Multi-modality canonical feature selection for Alzheimer's disease diagnosis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8674 LNCS (pp. 162 - 169).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, X., Suk, HI., & Shen, D. (2014). Sparse discriminative feature selection for multi-class Alzheimer’s Disease classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8679 (pp. 157 - 164).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Cai, H., Huang, Z., Zhu, X., Zhang, Q., & Li, X. (2014). Multi-output regression with tag correlation analysis for effective image tagging. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8422 LNCS (pp. 31 - 46).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, X., Suk, HI., & Shen, D. (2014). Matrix-similarity based loss function and feature selection for Alzheimer's disease diagnosis. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. (pp. 3089 - 3096).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, X., Huang, Z., Shen, HT., & Zhao, X. (2013). Linear cross-modal hashing for efficient multimedia search. MM 2013 - Proceedings of the 2013 ACM Multimedia Conference. (pp. 143 - 152).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhao, X., Li, X., Pang, C., Zhu, X., & Sheng, QZ. (2013). Online human gesture recognition from motion data streams. MM 2013 - Proceedings of the 2013 ACM Multimedia Conference. (pp. 23 - 32).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, X., Zhang, J., & Zhang, S. (2013). Mixed-norm regression for visual classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8346 LNAI (pp. 265 - 276).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhang, J., Zhu, X., Li, X., & Zhang, S. (2013). Mining item popularity for recommender systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8347 LNAI (pp. 372 - 383).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhu, X., Wu, X., Ding, W., & Zhang, S. (2013). Feature selection by joint graph sparse coding. Proceedings of the 2013 SIAM International Conference on Data Mining, SDM 2013. (pp. 803 - 811).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhang, S., Jin, Z., Zhu, X., & Zhang, J. (2009). Missing data analysis: A kernel-based multi-imputation approach. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5300 LNCS (pp. 122 - 142).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhang, S., Liu, L., Zhu, X., & Zhang, C. (2008). A strategy for attributes selection in cost-sensitive decision trees induction. Proceedings - 8th IEEE International Conference on Computer and Information Technology Workshops, CIT Workshops 2008. (pp. 8 - 13).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhang, S., Jin, Z., & Zhu, X. (2008). NIIA: Nonparametric iterative imputation algorithm. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5351 LNAI (pp. 544 - 555).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhang, C., Zhu, X., Zhang, J., Qin, Y., & Zhang, S. (2007). GBKII: An imputation method for missing values. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4426 LNAI (pp. 1080 - 1087).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhang, S., Zhu, X., Zhang, J., & Zhang, C. (2007). Cost-time sensitive decision tree with missing values. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4798 LNAI (pp. 447 - 459).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhang, C., Qin, Y., Zhu, X., Zhang, J., & Zhang, S. (2007). Clustering-based missing value imputation for data preprocessing. 2006 IEEE International Conference on Industrial Informatics, INDIN'06. (pp. 1081 - 1086).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Huang, HJ., Qin, Y., Zhu, X., Zhang, J., & Zhang, S. (2006). Difference detection between two contrast sets. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4081 LNCS (pp. 481 - 490).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhang, S., Qin, Y., Zhu, X., Zhang, J., & Zhang, C. (2006). Optimized parameters for missing data imputation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4099 LNAI (pp. 1010 - 1016).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.
Zhang, S., Zhang, J., Zhu, X., & Huang, Z. (2006). Identifying follow-correlation itemset-pairs. Proceedings - IEEE International Conference on Data Mining, ICDM. (pp. 765 - 774).
[Conference Paper in Published Proceedings]Authored by: Zhu, X.

Supervision and Teaching

Summary of Doctoral Supervision

Position Current Completed
Supervisor 4 0
Co-supervisor 1 0

Current Doctoral Supervision

Supervisor of:

  • Ayisha Shaik - Doctor of Philosophy
    Disease Detection with Machine Learning Techniques
  • Rongyao Hu - Doctor of Philosophy
    Sparse feature selection and its applications
  • Jinting Zhu - Doctor of Philosophy
    Self-paced learning and its applications
  • Tong Liu - Doctor of Philosophy
    Robust Spectral Clustering

Co-supervisor of:

  • Jiawei Zhao - Doctor of Philosophy
    Unsupervised neural machine translation

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