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. NOTE: 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.