Papers (full list)

Zhao, J.H.* (2024), 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.

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].

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.

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.