## What is the fisher linear discriminant method?

Fisher’s linear discriminant is a classification method that projects high-dimensional data onto a line and performs classification in this one-dimensional space. The projection maximizes the distance between the means of the two classes while minimizing the variance within each class.

**What is linear discriminant analysis in machine learning?**

Linear Discriminant Analysis or LDA is a dimensionality reduction technique. It is used as a pre-processing step in Machine Learning and applications of pattern classification. LDA is a supervised classification technique that is considered a part of crafting competitive machine learning models.

### What is discriminant function in Pattern Recognition?

Object Classification Methods Discriminant analysis is a very useful multivariate statistical technique which takes into account the different variables of an object and works by finding the so called discriminant functions in such a way that the differences between the predefined groups are maximized.

**What is linear discriminant analysis discuss with a suitable example?**

It is used for modeling differences in groups i.e. separating two or more classes. It is used to project the features in higher dimension space into a lower dimension space. For example, we have two classes and we need to separate them efficiently. Classes can have multiple features.

## What is the purpose of linear discriminant analysis?

Linear discriminant analysis is primarily used here to reduce the number of features to a more manageable number before classification. Each of the new dimensions is a linear combination of pixel values, which form a template.

**What is the goal of LDA?**

The aim of LDA is to maximize the between-class variance and minimize the within-class variance, through a linear discriminant function, under the assumption that data in every class are described by a Gaussian probability density function with the same covariance.

### Which is better PCA or LDA?

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.

**How is linear discriminant analysis used in machine learning?**

Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher’s linear discriminant, a method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events.

## What is the difference between LDA and Fisher’s linear discriminant?

Fisher’s linear discriminant. The terms Fisher’s linear discriminant and LDA are often used interchangeably, although Fisher’s original article actually describes a slightly different discriminant, which does not make some of the assumptions of LDA such as normally distributed classes or equal class covariances.

**How is linear discriminant analysis used in face recognition?**

In computerised face recognition, each face is represented by a large number of pixel values. Linear discriminant analysis is primarily used here to reduce the number of features to a more manageable number before classification.

### How is discriminant analysis different from factor analysis?

Discriminant analysis is also different from factor analysis in that it is not an interdependence technique: a distinction between independent variables and dependent variables (also called criterion variables) must be made. LDA works when the measurements made on independent variables for each observation are continuous quantities.