Howtosignalprocessing
Linear Discriminant Analysis or Normal Discriminant Analysis or Discriminant Function Analysis is a dimensionality reduction technique that is commonly used for supervised classification problems. It is used for modelling differences in groups i.e. separating two or more classes.
How is LDA different from PCA?
PCA is an unsupervised learning algorithm while LDA is a supervised learning algorithm. This means that PCA finds directions of maximum variance regardless of class labels while LDA finds directions of maximum class separability.
What does LDA tell us?
The results of an LDA give probability distributions for the topics over the vocabulary. In practice this means a list of words from the vocabulary, each with a probability associated with it. We can of course list the words in order of decreasing probability, and look at the top j words per topic for some j.
Is LDA better than PCA?
PCA performs better in case where number of samples per class is less. Whereas LDA works better with large dataset having multiple classes; class separability is an important factor while reducing dimensionality.
