In quadratic discriminant analysis, what assumption about the covariance matrices is relaxed compared to LDA? They are assumed to be equal They are assumed to be different They are assumed to be diagonal They are assumed to be identical

What theorem is fundamental to the concept of linear discriminant analysis? Central limit theorem No free lunch theorem Bayes' theorem Occam's razor

the discriminant of basic quadratic equation

In linear discriminant analysis, how are observations classified? Based on posterior probabilities Based on prior probabilities Based on residuals Based on the F-statistic

If a quadratic equation has a discriminant that equals zero, which of the following statements is always true?

In principal component analysis, the projected lower dimensional space corresponds to – A. subset of the original co-ordinate axis B. eigenvectors of the data covariance matrix C. eigenvectors of the data distance matrix D. orthogonal vectors to the original co-ordinate axis