How partial least square method differs from principal component regression method?

Partial Least Squares, use the annotated label to maximize inter-class variance. Principal components are pairwise orthogonal. Principal components are focus on maximize correlation. The main difference is that the PCA is unsupervised method and PLS is supervised method.

Is PLS better than PCA?

When a dependent variable for a regression is specified, the PLS technique is more efficient than the PCA technique for dimension reduction due to the supervised nature of its algorithm.

What is the difference between PCA and PLS DA?

PLS-DA is a supervised method where you supply the information about each sample’s group. PCA, on the other hand, is an unsupervised method which means that you are just projecting the data to, lets say, 2D space in a good way to observe how the samples are clustering by theirselves.

How is PCA different from linear regression?

With PCA, the error squares are minimized perpendicular to the straight line, so it is an orthogonal regression. In linear regression, the error squares are minimized in the y-direction. Thus, linear regression is more about finding a straight line that best fits the data, depending on the internal data relationships.

What is the purpose of PLS?

Partial least squares discriminant analysis (PLS-DA) is a variant used when the Y is categorical. PLS is used to find the fundamental relations between two matrices (X and Y), i.e. a latent variable approach to modeling the covariance structures in these two spaces.

Should you do PCA before Logistic regression?

If the variables are not measured on a similar scale, we need to do feature scaling before applying PCA for our data. This is because PCA directions are highly sensitive to the scale of the data. The most important part in PCA is selecting the best number of components for the given dataset.