What is the relationship between PCA and LDA Qda?

LDA focuses on finding a feature subspace that maximizes the separability between the groups. While Principal component analysis is an unsupervised Dimensionality reduction technique, it ignores the class label. PCA focuses on capturing the direction of maximum variation in the data set.

Does LDA use PCA?

LDA is like PCA which helps in dimensionality reduction, but it focuses on maximizing the separability among known categories by creating a new linear axis and projecting the data points on that axis.

Why PCA is used in face recognition?

PCA is a statistical approach used for reducing the number of variables in face recognition. In PCA, every image in the training set is represented as a linear combination of weighted eigenvectors called eigenfaces. A number of experiments were done to evaluate the performance of the face recognition system.

Which of the comparisons are true about PCA and LDA?

LDA explicitly attempts to model the difference between the classes of data. PCA on the other hand does not take into account any difference in class. PCA explicitly attempts to model the difference between the classes of data. LDA on the other hand does not take into account any difference in class.

Which comparison is true about PCA and LDA?

A. LDA explicitly attempts to model the difference between the classes of data. PCA on the other hand does not take into account any difference in class.

What is LDA in face recognition?

LDA makes use of projections of training images into a subspace defined by the fisher faces known as fiherspace. Recognition is performed by projecting a new face onto the fisher space, The KNN algorithm is then applied for identification.

What do you understand by Eigen faces what do they mathematically represent what is their role in dimensionality reduction?

The eigenfaces themselves form a basis set of all images used to construct the covariance matrix. This produces dimension reduction by allowing the smaller set of basis images to represent the original training images. Classification can be achieved by comparing how faces are represented by the basis set.

When should we use LDA?

It is used as a pre-processing step in Machine Learning and applications of pattern classification. The goal of LDA is to project the features in higher dimensional space onto a lower-dimensional space in order to avoid the curse of dimensionality and also reduce resources and dimensional costs.

Why is QDA better than LDA?

LDA (Linear Discriminant Analysis) is used when a linear boundary is required between classifiers and QDA (Quadratic Discriminant Analysis) is used to find a non-linear boundary between classifiers. LDA and QDA work better when the response classes are separable and distribution of X=x for all class is normal.