Sparse models by iteratively reweighted feature scaling: a framework for wavelength and sample selection

作者：Andries Erik^*

来源：Journal of Chemometrics, 2013, 27(3-4): 50-62.

DOI：10.1002/cem.2492

• multivariate calibration
• sparsity
• wavelength selection
• sample selection
• least absolute shrinkage and selection operator (LASSO)
• Tikhonov regularization (TR)
• support vector regression (SVR)

In the past decade, there has been an increase in the use of sparse multivariate calibration methods in chemometrics. Sparsity describes a parsimonious state of model complexity and can be defined in terms of a subset of samples or covariates (e.g., wavelengths) that are used to define the calibration model. With respect to their classical counterparts such as principal component regression or partial least squares, sparse models are more easily interpretable and have been shown to exhibit non-inferior prediction performance. However, sparse methods are still not as fast as the classical methods in spite of recent numerical advances. In addition, for many chemometricians, sparse methods are still black-box algorithms whose internal workings are not well understood. In this paper, we describe a simple framework whereby classical multivariate calibration methods can be iteratively used to generate sparse models. Moreover, this approach allows for either wavelength or sample sparsity. We demonstrate the effectiveness of this approach on two spectroscopic data sets.

• 出版日期2013-4