• Corpus ID: 8370473

Fisher Linear Discriminant Analysis

@inproceedings{Li2014FisherLD,
  title={Fisher Linear Discriminant Analysis},
  author={Cheng Li},
  year={2014}
}

• Cheng Li
• Published 2014
• Computer Science

Fisher Linear Discriminant Analysis (also called Linear Discriminant Analysis(LDA)) are methods used in statistics, pattern recognition and machine learning to find a linear combination of features which characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification. LDA is closely related to PCA, for both of them are based on linear, i.e. matrix multiplication, transformations. For the case of PCA, the transformation is baed on minimizing mean square error between original data vectors and data vectors that can be estimated fro the reduced dimensionality data vectors. And the PCA does not take into account any difference in class. But for the case of LDA, the transformation is based on maximizing a ratio of “between-class variance” to “within-class variance” with the goal of reducing data variation in the same class and increasing the separation between classes. Let’s see an example of LDA as below(Figure1):