Milestone: PCA_1

This Milestone will allow you to run full PCA

Depends: MATRIX_OPERATIONS

Which library provides a way to compute full PCA/SVD?
Load the dataset ######.csv as a matrix.
What are the dimensions of your data matrix?
Compute PCA on it (use scale and centering equal to TRUE). PCA will result in 2 matrices and 1 vector.
Check the dimensions of the matrices and vectors from PCA.
How many principal components do we have for this dataset?
Where is stored the variance captured by each PC?
What does the matrix samples-by-PCs mean?
What does the matrix features-by-PCs mean?
What happens if you transpose the data matrix before computing PCA?
Check the dimensions of the resulting matrices and vectors from PCA.
How many principal components do we have for this dataset?
Where is stored the variance captured by each PC?
What does the matrix samples-by-PCs mean?
What does the matrix features-by-PCs mean?

Milestone: PCA_2

This Milestone will allow you to run truncated PCA

Depends: PCA

In the method above, we used a library to compute full PCA/SVD. However, in many cases computing all PCs takes a long time, so to compute only the first say 20 PCs, we can compute a randomised truncated PCA/SVD. Which library provides this PCA implementation?
Load the dataset ######.csv as a matrix.
What are the dimensions of your data matrix?
Compute PCA on it (use scale and centering equal to TRUE). PCA will result in 2 matrices and 1 vector.
Check the dimensions of the matrices and vectors from PCA.
How many principal components do we have for this dataset?
Where is stored the variance captured by each PC?
What does the matrix samples-by-PCs mean?
What does the matrix features-by-PCs mean?
What is the advantage of running truncated PCA instead of full PCA?

This Milestone will allow you to get a deeper understanding of the power of PCA

Depends: PCA_2