Linear Discriminant Analysis  Python for Integrated Circuits   An Online Book  

Python for Integrated Circuits http://www.globalsino.com/ICs/  


Chapter/Index: Introduction  A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  Appendix  
================================================================================= Linear Discriminant Analysis (LDA) is a supervised machine learning technique used for dimensionality reduction and classification. It seeks to find a linear combination of features that maximizes the separation between different classes or categories in a labeled dataset. LDA identifies new variables, called linear discriminants, that capture the most discriminative information in the data, making it easier to classify data points into their respective classes while reducing the dimensionality of the feature space. It is particularly useful when you have multiple classes and want to transform your data into a lowerdimensional space that preserves classrelated variations. In Linear Discriminant Analysis, the random variables X and Y typically refer to the features or attributes of your dataset, where X represents the features or attributes, and Y represents the class labels or target variable. LDA is a supervised dimensionality reduction and classification technique that seeks to find a linear combination of features (X) that best separates different classes or categories (Y) in a dataset. Here's how LDA works:
Therefore, in LDA, X represents the original features or attributes, and Y represents the class labels or categories you are trying to discriminate between. The goal of LDA is to find linear combinations of X (the linear discriminants) that maximize the separation between the classes represented by Y. These linear discriminants are determined through LDA's mathematical computations and are used for dimensionality reduction and classification purposes. Table 3964. Applications and related concepts of Linear Discriminant Analysis.
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