Programming Exercise 7: K-means Clustering and Principal Component Analysis Summary PDF main program: ex7.m 1 K-means Clustering 1.1 Implementing K-means 1.1.1 Finding closest centroids findClosestCentroids.m 1.1.2 Computing centroid means computeCentroids.m 1.2 K-means on example dataset None 1.3 Random initialization kMeansInitCentroids.m 1.4 Image compression with K-means 1.4.1 K-means on pixels in next problem(1.5), have similar code 1.5 Optional (ungraded) exercise: Use your own image ex7.m line: 175 emma_1.png emma_2.png emma_3.png 2 Principal Component Analysis main program ex7_pca.m 2.1 Example Dataset None 2.2 Implementing PCA pca.m 2.3 Dimensionality Reduction with PCA 2.3.1 Projecting the data onto the principal components projectData.m 2.3.2 Reconstructing an approximation of the data recoverData.m 2.3.3 Visualizing the projections None 2.4 Face Image Dataset 2.4.1 PCA on Faces None 2.4.2 Dimensionality Reduction None 2.5 Optional (ungraded) exercise: PCA for visualiza- tion None
