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Linear Discriminant Analysis (LDA) is used for pre-processing in Machine Learning. It is applied to classify the patterns since the primary purpose of LDA is to predict the features in higher dimensional space to a lower-dimensional one to avoid dimensionality loss and to reduce dimensional and resources costs. The common application is identification. LDA can be applied to identify the type of customers that would buy a certain kind of product from a store.

Linear discriminant analysis is used to decrease the number of features to a manageable quantity prior to classification. Every new dimension is a linear pixel value combination forming a template.

LDA carves a multidimensional space and is mainly used in classification problems at points of categorical output variables. Both binary classification and multi-class classification use the standard LDA model through Gaussian Distribution of input variables. On the other hand, PCA is used to slump multidimensional space. It is predominantly used to reduce dimensionality techniques in fields such as facial recognition, computer vision and image compression. It also finds its application in domains like finance, bioinformatics, data mining, psychology, etc.

If for classification, then you are suggested to avoid PCA since it is used to reduce dimensionality. But you can anyways use it to improve the performance of LDA.

Linear discriminant analysis (LDA) is a supervised machine learning technique. It is a commonly used subspace learning method.