Function Implementations:Pattern Recognition

1. Implementing fisher LDA method using MATLAB.

- Write functions that take a data set and compute the optimal projection vector following the fisher criterion.

- The input set of instances can be of two dimensions or of more than two dimensions.

- The output from the main function must be the identified projection vector.

2. Implementing perceptron method using MATLAB.

- Write functions that take a set of examples and identify the hyper plane to separate the examples

- The input set of instances can be of two dimensions or of more than two dimensions.

- The output from the main function must be the identified hyper plane.

3. Write functions to generate synthetic data for evaluating the LDA and perceptron methods using MATLAB.

- The functions should be able to allow user to specify how many dimensions of each instance, how many classes, and how many examples in each class.

- The functions should generate random instances and provide a label for each instance.

- Use the examples generated from the functions to evaluate the two methods developed in 1 and 2. Conduct cross-validation and report the average accuracy, sensitivity, and specificity.


a. There is no requirement to develop a GUI to visualize the intermediate or the final results, although a visualization is recommended.

b. The synthetic data generated using the functions from problem 3 (above) are suggested to be used for evaluating the two methods from problems 1 and 2.

A report that includes the following items is due in addition to the source code for the functions:

- A description of each function,

- How to run the functions to get the reported results, and

- The experimental results.

Skills: Artificial Intelligence, Face Recognition, FPGA, Machine Learning, Matlab and Mathematica

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