Disciriminant analysis is the classical method to solve Pattern Recognition.
In this method the values of average and standard deviation is important. And normal distribution is used as the image of the data. So if the central points of groups have the meaning, this method is suitable.
There are two ways to solve the discrimination.
The idea uses a line as the boundary of categories.
Support Vector Machine is youger method using a line to solve problem.
Discriminant Analysis finds the best line to separate the distributions clearly.
Support Vector Machine find the best line to separate the data near the line clearly.
Mahalanobis' Distance
is used to calculate the distance between the new data and each central point of the groups.
The idea, "distance from the central point" uses the image of normal distribution.
This approach can calculate the probability of the categories of the new data.
We can also calculate the probability by Logistic Regression Analysis . By Logistic Regression Analysis, the sum of probability of two category on the same point is 1.
But by Discriminant Analysis, the sum of probability of two category on the same point is not 1.
Difference of Good Distribution by Methods
Difference of Output by Methods
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Logistic Regression Analysis
