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Whereas One-Hot Encoding is used to convert categorical variables into a number which can be understood by the ML algorithm.
LDA PCA and FA are used for dimensionality reduction in your feature set. So you cannot use these in place of one-hot encoding.
The differences between LDA, PCA & FA are:
Linear Discriminant Analysis(LDA) uses information of classes to find new features in order to maximize the class separability (between class-variance). LDA works when the measurements made on independent variables for each observation are continuous quantities
Principle Component Analysis(PCA) uses the variance of each feature and identifies the components (within variable variance)
Factor Analysis(FA) builds the feature combinations based on differences rather than similarities. It tries to uncover the latent factors which account for the variance shared between the observed variables.