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After learning the Fisher linear discriminant in class and its ability to project data into one dimension so that it can be separated by a threshold, I wanted to evaluate it using non linearly separable data and to observe visually its performance. So I implemented the Fisher discriminant in Matlab and used some synthetic data to visualize how the data is projected into one dimension. | After learning the Fisher linear discriminant in class and its ability to project data into one dimension so that it can be separated by a threshold, I wanted to evaluate it using non linearly separable data and to observe visually its performance. So I implemented the Fisher discriminant in Matlab and used some synthetic data to visualize how the data is projected into one dimension. | ||
| − | The following plots show the results of my experiment, where the red and the blue circles represent data of two different classes in 2D and the green line represents the direction of the vector w. | + | The following plots show the results of my experiment, where the red and the blue circles represent data of two different classes in 2D and the green line represents the direction of the vector w where the data is projected on. |
| − | [[Image: | + | [[Image:fig_1.jpg]] [[Image:fig_2.jpg]] [[Image:fig_3.jpg]] |
Revision as of 18:15, 15 April 2010
After learning the Fisher linear discriminant in class and its ability to project data into one dimension so that it can be separated by a threshold, I wanted to evaluate it using non linearly separable data and to observe visually its performance. So I implemented the Fisher discriminant in Matlab and used some synthetic data to visualize how the data is projected into one dimension.
The following plots show the results of my experiment, where the red and the blue circles represent data of two different classes in 2D and the green line represents the direction of the vector w where the data is projected on.
