In this exercise, you will implement the K-means clustering algorithm and apply it to compress an image. In the second part, you will use principal component analysis to find a low-dimensional representation of face images. Files needed for this exercise -

1. ex7.mlx - MATLAB Live Script that steps you through the exercise
2. ex7data1.mat - Example Dataset for PCA
3. ex7data2.mat - Example Dataset for K-means
4. ex7faces.mat - Faces Dataset
5. bird small.png - Example Image
6. displayData.m - Displays 2D data stored in a matrix
7. drawLine.m - Draws a line over an exsiting figure
8. plotDataPoints.m - Initialization for K-means centroids
9. plotProgresskMeans.m - Plots each step of K-means as it proceeds
10. runkMeans.m - Runs the K-means algorithm
11. *pca.m - Perform principal component analysis
12. *projectData.m - Projects a data set into a lower dimensional space
13. *recoverData.m - Recovers the original data from the projection
14. *findClosestCentroids.m - Find closest centroids (used in K-means)
15. *computeCentroids.m - Compute centroid means (used in K-means)
16. *kMeansInitCentroids.m - Initialization for K-means centroids

If you have any doubt or for feedback, feel free to comment in discussion section.