1. Evaluate PCA and ISOMAP.
- Get a copy of PCA and ISOMAP implementation in MATLAB.
- Download 3 data sets from UCI repository. Note that the dimensionality of each data set is preferably greater than 10.
- Evaluate the PCA and ISOMAP method with the data sets. Be creative how to evaluate and discuss your observations.
2. Design a mini project and use the methods like (AdaBoosting, Bagging, fuzzy c means, k means, LDA, perceptron) but not limited to those methods to achieve a clustering or classification objective.
- Make necessary changes to the code and implement additional ones when needed.
- Evaluate the programs with either synthetic data sets or real-world data sets.
- Write a report to discuss the problem, the implementation, and the results.
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
- discussion of the experimental results.