On the use of conditional maximization in chemometrics

The purpose of this short communication is to illustrate the use of conditional maximization (CM) in chemometric applications. The CM algorithm is useful in reducing the computational complexity when a high‐dimensional and complicated maximization problem arises from fitting chemometric models. It can also be efficiently combined with the expectation‐maximization (EM) algorithm for handling incomplete data, a problem that sometimes arises when only a part of the intended data can be collected. Three models from fluorescence spectroscopy are used for illustration.