代写计算机视觉与成像 机器人视觉代写 MATLAB代码代写

Computer Vision & Imaging/ Robot Vision – Formative task

代写计算机视觉与成像 This task is formative. For this task, you will need to submit the following files: • code username formativetask3.m.

This task is formative. For this task, you will need to submit the following files:

• code username formativetask3.m.

• PDF answer sheet username formativetask3.pdf. (Optional)

The aim of this task is to make students be familiar with Principal Component Analysis (PCA) using MATLAB built-in functions. It’s not necessary to implement a custom PCA function to complete this task.

Question 1 代写计算机视觉与成像

Load the League of Legends Champions stats data from data.csv. Normalise the data and apply principal component analysis to calculate the principal component coefficients.

Visualise the first and second principal components with a loading plot, explain the meaning of a loading plot, and describe what kind of information you can obtain from the loading plot.

Question 2 代写计算机视觉与成像

Alternatively, use a bar chart to illustrate the principal component coefficients, and compare it with the loading plot: what’s the pros and cons of a loading plot and a bar chart?

Question 3

Calculate total variance of all principal components, and visualise the accumulative variance with each principal component. Describe how much does the first two principal components explained from the data.

Question 4 代写计算机视觉与成像

Perform dimensionality reduction to the data, and visualise the data on a score plot using the first two principal components. Use different color to distinguish champions with different type. Judging by the champions’ types, how well does PCA explain the given dataset?

See below the expected results of each question.

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