![]() pca.m - Perform principal component analysis Submit.m - Submission script that sends your solutions to our servers PlotProgresskMeans.m - Plots each step of K-means as it proceeds PlotDataPoints.m - Initialization for K-means centroids ex7.m - Octave/MATLAB script for the first exercise on K-meansĮx7 pca.m - Octave/MATLAB script for the second exercise on PCAĮx7data2.mat - Example Dataset for K-meansĭisplayData.m - Displays 2D data stored in a matrixĭrawLine.m - Draws a line over an exsiting figure.Setup Instructions” of the course website. You can also find instructions for installing Octave/MATLAB in the “Environment This directory before starting this exercise. If needed, use the cd command in Octave/MATLAB to change to ![]() To get started with the exercise, you will need to download the starterĬode and unzip its contents to the directory where you wish to complete theĮxercise. The video lectures and completing the review questions for the associated In the second part, you will use principalĬomponent analysis to find a low-dimensional representation of face images.īefore starting on the programming exercise, we strongly recommend watching ![]() In this exercise, you will implement the K-means clustering algorithm andĪpply it to compress an image. In this exercise, you will implement K-means Clustering and Principal Component Analysis.įiles included in this exercise can be downloaded here ⇒ : Download
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