Develop MATLAB Script For Loading Data, Dividing It, Applyin

Develop MATLAB script for loading data, dividing it, applying Fisher LDA, and evaluating its performance

This assignment involves multiple tasks related to image recognition and statistical analysis using MATLAB. The core components include loading and processing a dataset of character images, splitting the data into training and testing sets, implementing Fisher Linear Discriminant Analysis (LDA) to find an optimal projection vector for class separation, and evaluating the LDA model's performance through cross-validation. Each task requires specific MATLAB functions and scripts, with emphasis on data handling, statistical computation, and performance metrics.

Paper For Above instruction

The primary goal of this assignment is to develop a MATLAB-based pipeline that effectively loads a complex image dataset, isolates key features, and applies Fisher Linear Discriminant Analysis for binary classification—specifically distinguishing between the letters "A" and "X". Such tasks are fundamental in pattern recognition, machine learning, and image analysis, enabling better understanding of statistical classification techniques and their practical applications.

Initially, the task requires writing a MATLAB script that loads image data stored in a file, then displays the first five instances for visual inspection. The data is represented as numerical attributes, including shape parameters and pixel statistics, which collectively serve as features for classification. Once loaded, the script must create a matrix from these instances, ensuring efficient data handling.

Subsequently, the dataset must be partitioned into two subsets: 70% for training and 30% for testing, to evaluate the model's performance on unseen data. This division involves random sampling to prevent bias and must store these subsets in variables named 'TrainingSet' and 'TestingSet'. Both datasets should then be saved into a MATLAB .mat file for future use, enabling reproducibility and ease of loading in subsequent analysis steps.

The core statistical technique, Fisher LDA, requires implementing a MATLAB function that computes the optimal projection vector for class separation. The Fisher criterion aims to maximize the ratio of between-class variance to within-class variance in the projected space, thus producing a linear boundary that best separates the classes. The function should take the training data as input and output the projection vector, ensuring it can be reused for classification tasks.

Furthermore, the model's robustness and generalization ability must be assessed through cross-validation. This involves partitioning the data multiple times into training and validation folds, applying the LDA projection, and measuring classification accuracy, sensitivity, and specificity across each iteration. The evaluation script then computes the average and standard deviation of these metrics, providing insight into the reliability and effectiveness of the Fisher LDA method applied to this dataset.

Alongside the functional code, a comprehensive report is required. This report should include detailed descriptions of each MATLAB function, instructions on how to execute the scripts to reproduce the reported results, and a summary of the experimental outcomes. No graphical user interface is necessary, but clarity and sound programming practices are essential for clarity and reproducibility of the analysis.

Executing this assignment enhances understanding of supervised classification techniques, MATLAB programming for machine learning tasks, and performance evaluation methods—skills that are critical in research and practical applications in image recognition and pattern classification domains.

References

  • Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.
  • Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern Classification (2nd ed.). Wiley-Interscience.
  • Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer.
  • Jolliffe, I. T. (2002). Principal Component Analysis (2nd ed.). Springer.
  • Ball, K., & Hall, K. (1965). ISODATA, a novel method of data analysis and pattern classification. Stanford Research Institute.
  • Witten, I. H., & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
  • Hastie, T., Tibshirani, R., & Friedman, J. (2015). Statistical Learning with Sparsity: The Lasso and Generalizations. CRC Press.
  • Haykin, S. (2009). Neural Networks and Learning Machines. Pearson.
  • Müller, K. R., & Guido, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly Media.
  • Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.

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