主要论文论著 | 近五年第一作者或通讯作者代表作： [1]Li H, Wu P, Ding S, et al. A knowledge-informed burst-sparsity learning (BSL) with non-uniform pattern-coupled prior for spectroscopic regression [J]. Chemometrics and Intelligent Laboratory Systems, 2025, 262: 105378. [2]Li H, Jiang Y, Wu P, et al. Bayesian Semi-Supervised Learning (BSSL) for spectral variable selection[J]. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2025: 126483. [3]Wu, P., Chen, T., Wang, M., Xing, L., Zou, X., Li, H.* Hierarchical clustering and optimal interval combination (HCIC): a knowledge-guided strategy for consistent and interpretable spectral variable interval selection. Analytical Methods,2025, 17(18), 3793-3805. [4]Wu, P., Chen, T., Jiang Y, Zhang, Y., Zou, X., Li, H.* A Bayesian Adaptive Clustered Prior Learning (ACPL) Method for Sparse Spectroscopic Regression[J]. Analytica Chimica Acta, 2025 （Accept） [5]Li H, Wu P, Dai J, et al. A Monte Carlo resampling based multiple feature-spaces ensemble (MFE) strategy for consistency-enhanced spectral variable selection[J]. Analytica Chimica Acta, 2023, 1279: 341782. [6]Li H, Wu P, Dai J, et al. Discriminating compounds identification based on the innovative sparse representation chemometrics to assess the quality of Maofeng tea[J]. Journal of Food Composition and Analysis, 2023, 123: 105590. [7]Li H, Chen S, Dai J, et al. Fast Burst-Sparsity Learning-Based Baseline Correction (FBSL-BC) Algorithm for Signals of Analytical Instruments[J]. Analytical Chemistry, 2022, 94(12): 5113-5121 [8]Li H, Dai J, Xiao J, et al. Spectral variable selection based on least absolute shrinkage and selection operator with ridge-adding homotopy[J]. Chemometrics and Intelligent Laboratory Systems, 2022, 221: 104487. [9]Li H, Dai J, Pan T, et al. Sparse Bayesian learning approach for baseline correction[J]. Chemometrics and Intelligent Laboratory Systems, 2020, 204: 104088. [10]Li H, Wang B, Dai J, et al. Ridge-Adding Homotopy Approach for l 1-norm Minimization Problems[J]. IEICE TRANSACTIONS on Information and Systems, 2020, 103(6): 1380-1387. |
