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. Before starting on the programming exercise, we strongly recommend watch- ing the video lectures and completing the review questions for the associated topics.
To get started with the exercise, you will need to download the starter code and unzip its contents to the directory where you wish to complete the exercise. If needed, use the cd command in Octave/MATLAB to change to this directory before starting this exercise.
You can also find instructions for installing Octave/MATLAB in the “En- vironment Setup Instructions” of the course website.
Files included in this exercise
ex7.m - Octave/MATLAB script for the first exercise on K -means ex7 pca.m - Octave/MATLAB script for the second exercise on PCA ex7data1.mat - Example Dataset for PCA
ex7data2.mat - Example Dataset for K -means
ex7faces.mat - Faces Dataset
bird small.png - Example Image
displayData.m - Displays 2D data stored in a matrix drawLine.m - Draws a line over an exsiting figure plotDataPoints.m - Initialization for K -means centroids plotProgresskMeans.m - Plots each step of K -means as it proceeds
runkMeans.m - Runs the K -means algorithm
submit.m - Submission script that sends your solutions to our servers
[?] pca.m - Perform principal component analysis
[?] projectData.m - Projects a data set into a lower dimensional space
[?] recoverData.m - Recovers the original data from the projection
[?] findClosestCentroids.m - Find closest centroids (used in K -means) [?] computeCentroids.m - Compute centroid means (used in K -means) [?] kMeansInitCentroids.m - Initialization for K -means centroids
? indicates files you will need to complete
Throughout the first part of the exercise, you will be using the script ex7.m, for the second part you will use ex7 pca.m. These scripts set up the dataset for the problems and make calls to functions that you will write. You are only required to modify functions in other files, by following the instructions in this assignment.
Submission and Grading
After completing various parts of the assignment, be sure to use the submit function system to submit your solutions to our servers. The following is a breakdown of how each part of this exercise is scored.
Part
Submitted File
Points
Find Closest Centroids
Compute Centroid Means
findClosestCentroids.m
computeCentroids.m
30 points
30 points
PCA
Project Data
Recover Data
pca.m
projectData.m recoverData.m
20 points
10 points
10 points
Total Points
100 points
You are allowed to submit your solutions multiple times, and we will take only the highest score into consideration.
