Unlike PCA, which is unsupervised, FLD uses labels associated with data points, and no doubt it may get better linear features and accuracy than PCA. We start by illustrating this motivation, and practice the problem-solving framework by gradually developing the correct mathematical formulation behind the relatively simple idea behind Fisher's linear discriminant (FLD). We discuss various practical issues: the solution for the binary case, the scenario where this solution breaks down, and how to generalize from tasks with only two categories to many categories.
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