College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning Province, China.
Technology Center, China Tobacco Zhejiang Industrial Co., Ltd, Hangzhou 310008, Zhejiang Province, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2023 May 5;292:122418. doi: 10.1016/j.saa.2023.122418. Epub 2023 Jan 26.
In chemometrics, calibration model adaptation is desired when training- and test-samples come from different distributions. Domain-invariant feature representation is currently a successful strategy to realize model adaptation and has received wide attention. The paper presents a nonlinear unsupervised model adaptation method termed as domain adaption regularization-based kernel partial least squares regression (DarKPLS). DarKPLS aims to minimize the source and target distributions in a low-dimensional latent space projected from the reproducing kernel Hilbert space (RKHS) generated with the labeled source data and unlabeled target data. Specially, the distributional means and variances between source and target latent variables are aligned in the RKHS. By extending existing domain invariant partial least square regression (di-PLS) with the projected maximum mean discrepancy (PMMD) to reduce the mean discrepancy in the RKHS further, DarKPLS could realize fine-grained domain alignment that further improves the adaptation performance. DarKPLS is applied to the γ-polyglutamic acid fermentation dataset, tobacco dataset and corn dataset, and it demonstrates improved prediction results in comparison with No adaptation partial least squares (PLS), null augmented regression (NAR), extended linear joint trained framework (ExtJT), scatter component analysis (SCA) and domain-invariant iterative partial least squares (DIPALS).
在化学计量学中，当训练样本和测试样本来自不同的分布时，需要对校准模型进行适应性调整。目前，域不变特征表示是实现模型自适应的一种成功策略，受到了广泛关注。本文提出了一种称为基于域自适应正则化的核偏最小二乘回归（DarKPLS）的非线性无监督模型自适应方法。DarKPLS 的目的是在从带标签的源数据和未标记的目标数据生成的再生核希尔伯特空间（RKHS）中投影的低维潜在空间中最小化源和目标分布。特别地，源和目标潜在变量之间的分布均值和方差在 RKHS 中对齐。通过将扩展现有域不变偏最小二乘回归（di-PLS）与投影最大均值差异（PMMD）相结合，进一步减少 RKHS 中的均值差异，DarKPLS 可以实现更精细的域对齐，从而进一步提高适应性能。DarKPLS 应用于 γ-聚谷氨酸发酵数据集、烟草数据集和玉米数据集，并与无适应部分最小二乘（PLS）、空增回归（NAR）、扩展线性联合训练框架（ExtJT）、散射分量分析（SCA）和域不变迭代偏最小二乘（DIPALS）相比，展示了改进的预测结果。
