Papers

Zhao, J.H.* (2025), Minorization-Maximization for Ratio Sum Linear Discriminant Analysis,
Submitted.

Ma, X.#+, Zhao, J.H.*+, Shang, C.C.+, Jiang, F.+, and Yu, P.L.H.+ (2025), Robust bilinear factor analysis based on the matrix-variate t distribution,
Submitted, [arXiv: 2401.02203].

Wang, Y.#+, Zhao, J.H.*+, Jiang, F.+, Shi, L.+, and Pan, J.X.+ (2025), A novel robust meta-analysis model using the t distribution for outlier accommodation and detection,
Research Synthesis Methods, Accepted, DOI: 10.1017/rsm.2025.8 [code].

Zhao, J.H.*+, Shang, C.C#+, Li, S.L.+, Xin, L.+, and Yu, P.L.H.+ (2025), Choosing the number of factors in factor analysis with incomplete data via a hierarchical Bayesian information criterion,
Advances in Data Analysis and Classification, Accepted, DOI: 10.1007/s11634-024-00582-w.

Yang, Z.J., Chen, W.Y., Zhang, H., Xu, Y.T., Shi, L., Zhao J.H. (2024), A Safe Screening Rule with Bi-level Optimization of ν Support Vector Machine,
Pattern Recognition, 155, 110644, DOI: 10.1016/j.patcog.2024.110644.

Shang, C.C.#, Ma, X, Jiang, F. and Zhao, J.H.*, (2024), Bilinear factor analysis,
Journal of Systems Science and Mathematical Sciences (in Chinese), 44(4), 1159-1188, DOI: 10.12341/jssms23125.

Zhao, J.H.*+, Liang, H.Y.#+, Li, S.L.+, Yang, Z.J.+ and Wang, Z.* (2024), Matrix-based vs. Vector-based Linear Discriminant Analysis: A Comparison of Regularized Variants on Multivariate Time Series Data,
Information Sciences, 654, 119872, DOI: 10.1016/j.ins.2023.119872.

Zhao, J.H.*, Ma, X#, Shi, L. and Wang, Z. (2023), Robust Bilinear Probabilistic PCA using a Matrix Variate t distribution,
IEEE Transactions on Neural Networks and Learning Systems, 34(12), 10683-10697, DOI: 10.1109/TNNLS.2022.3170797.

Ma, X.#, Zhao, J.H.*, Wang, Y., Shang, C.C., and Jiang, F. (2023), Robust factored principal component analysis for matrix-valued outlier accommodation and detection,
Computational Statistics & Data Analysis, 179, 107657, DOI: 10.1016/j.csda.2022.107657, [code].

Jiang, R.X. and Yang, Z.J. and Zhao, J.H. (2023) A Complete Deep Support Vector Data Description for One Class Learning,
IEEE Access, 11, 117494-117507, DOI: 10.1109/ACCESS.2023.3325734.

Jin, L, Chiu, S.N., Zhao, J.H., and Zhu, L.X. (2023), A Constrained Maximum Likelihood Estimation for Skew Normal Mixtures,
Metrika, 86, 391–419, DOI: 10.1007/s00184-022-00873-2.

Zhao, J.H.*, Sun,F., Liang H.Y., Ma, X., Li X.X., and He, J. (2021), Pseudo Bidirectional Linear Discriminant Analysis for Multivariate Time Series Classification,
IEEE Access, 9, 88674-88684, DOI: 10.1109/ACCESS.2021.3089839.

Jin, L.B., Dai, X.W., Shi, L., and Zhao, J.H. (2018), Generalized spatial outlier modified model and its applications,
Journal of Applied Statistics and Management (in Chinese), 37(2), 255-263, DOI: 10.13860/j.cnki.sltj.20171131-001.

Shi, L., J., Zhao, J.H., and Chen, G.M. (2016), Case deletion diagnostics for GMM estimation,
Computational Statistics & Data Analysis, 95(3), 176-191, DOI:10.1016/j.csda.2015.10.003.

Zhao, J.H., Shi, L.*, and Zhu, J. (2015), Two-Stage Regularized Linear Discriminant Analysis for 2-D Data,
IEEE Transactions on Neural Networks and Learning Systems, 26(8), 1669-1681, DOI: 10.1109/TNNLS.2014.2350993.

Zhao, J.H., Jin, L.B. and Shi, L.* (2015), Mixture model selection via hierarchical BIC,
Computational Statistics & Data Analysis, 88(8), 139-153, DOI: 10.1016/j.csda.2015.01.019.

Shi, L., Rahman, M.M., Gan, W., and Zhao, J.H. (2015), Stepwise local influence in generalized autoregressive conditional heteroskedasticity models,
Journal of Applied Statistics, 42(2), 428-444, DOI: 10.1080/02664763.2014.957661.

Zhao, J.H.* (2014), Efficient Model Selection for Mixtures of Probabilistic PCA via Hierarchical BIC,
IEEE Transactions on Cybernetics, 44(10), 1871-1883, DOI: 10.1109/TCYB.2014.2298401.

Zhao, J.H. and Shi, L.* (2014), Automated learning of factor analysis with complete and incomplete data,
Computational Statistics & Data Analysis, 72(4), 205-218, DOI: 10.1016/j.csda.2013.11.008.

Zhao, J.H.*, Yu, P.L.H., Shi, L. and Li, S.L. (2012), Separable linear discriminant analysis,
Computational Statistics & Data Analysis, 56(12), 4290-4300, DOI: 10.1016/j.csda.2012.04.003.

Zhao, J.H.*, Yu, P.L.H. and Kwok, James T. (2012), Bilinear Probabilistic Principal component analysis,
IEEE Transactions on Neural Networks and Learning Systems, 23(3), 492-503, DOI: 10.1109/TNNLS.2012.2183006.

Zhao, J.H.* and Yu, P.L.H. (2009), A note on variational Bayesian factor analysis,
Neural Networks, 22(7), 988-997, DOI: 10.1016/j.neunet.2008.11.002.

Zhao, J.H.*, Yu, P.L.H. and Jiang, Q.B. (2008), ML estimation for factor analysis: EM or non-EM?,
Statistics and Computing, 18(2), 109-123, DOI: 10.1007/s11222-007-9042-y.

Zhao, J.H.* and Yu, P.L.H. (2008), Fast ML estimation for the Mixture of Factor Analyzers via an ECM Algorithm,
IEEE Transactions on Neural Networks, 19(11), 1956-1961, DOI: 10.1109/TNN.2008.2003467.

Zhao, J.H.* and Jiang, Q.B. (2006), Probabilistic PCA for t distributions,
Neurocomputing, 69, 2217–2226, DOI: 10.1016/j.neucom.2005.07.011.

Shi, L., Shen, C., Shi, Q., Wang, Z., Zhao, J.H., Li, X.L., and Boccaletti, S. (2020), Recovering Network Structures Based on Evolutionary Game Dynamics via Secure Dimensional Reduction,
IEEE Transactions On Network Science and Engineering, 7(3), 2027-2036, DOI:10.1109/TNSE.2020.2970997.

王明锋、 赵建华、 兰海强、 宫玉鹏、 朱保昆(2014), 卷烟感官评价中小组得分的两阶段统计法,
烟草科技, 05期, pp 13-16, DOI: 10.3969/j.issn.1002-0861.2014.05.003.

王明锋、 焦玉磊、 赵建华、 朱保昆、 王坚、 者为(2014), 卷烟感官舒适性与主流烟气化学成分的回归建模,
中国烟草学报, 05期, pp 12-18.

武怡、曾晓鹰、朱保昆、赵建华、 王明锋(2012), 中式卷烟风格感官评价方法区域适应性分析,
烟草科技, 09期, pp 5-9, DOI: 10.3969/j.issn.1002-0861.2012.09.001.

朱保昆、王明锋、韩毅、焦玉磊、郭婧超、赵建华、 廖头根(2012), 烤烟主要烟气化学成分对卷烟感官舒适度的影响研究,
云南大学学报(自然科学版), 01期, pp 77-83.

朱保昆、朱东来、王明锋、赵建华、廖头根(2011), 烤烟主要化学指标与感官舒适度的相关性分析,
中国烟草科学, 06期, pp 17-20+25, DOI: 10.3969j.issn.1007-5119.2011.06.004.