4.5 Exercises

  1. Consider the following data in \(\mathbb{R}^2\)

\[\mathbf x_1 =\begin{pmatrix}1\\-1\end{pmatrix},\; \mathbf x_2 =\begin{pmatrix}-1\\1\end{pmatrix}, \;\mathbf x_3 =\begin{pmatrix}2\\2\end{pmatrix}\]

  • What is the orthogonal projection of these points onto \[\mathbf u_1 = \begin{pmatrix}1\\0\end{pmatrix}\] and onto \[\mathbf u_2 =\frac{1}{\sqrt{5}}\begin{pmatrix}1\\2\end{pmatrix}?\]

  • Compute the sample variance matrix of the data points, and compute its spectral decomposition.

  • Which unit vector \(\mathbf u\) would maximize the variance of these projections?

  • What vector \(\mathbf u\) would minimize \[\sum_{i=1}^4 ||\mathbf x_i -\mathbf u\mathbf u^\top \mathbf x_i||^2_2?\] This is the sum of squared errors from a rank 1 approximation to the data.

  • Plot the data points and convince yourself that your answers make intuitive sense.

  1. Consider a population covariance matrix \(\boldsymbol{\Sigma}\) of the form \[\boldsymbol{\Sigma}=\gamma \mathbf I_p + \mathbf a\mathbf a^\top\] where \(\gamma>0\) is a scalar, \(\mathbf I_p\) is the \(p \times p\) identity matrix and \(\mathbf a\) is a vector of dimension \(p\).
    • Show that \(\mathbf a\) is an eigenvector of \(\boldsymbol{\Sigma}\).
    • Show that if \(\mathbf b\) is any vector such that \(\mathbf a^\top \mathbf b=0\), then \(\mathbf b\) is also an eigenvector of \(\boldsymbol{\Sigma}\). What is the eigenvalue corresponding to \(\mathbf b\)?
    • Obtain all the eigenvalues of \(\boldsymbol{\Sigma}\).
    • Determine expressions for the proportion of variability ‘explained’ by:
    1. the largest (population) principal component of \(\boldsymbol{\Sigma}\);
    2. the \(r\) largest (population) principal components of \(\boldsymbol{\Sigma}\), where \(1 < r \leq p\).
  2. A covariance matrix has the following eigenvalues:
##  [1] 4.22 2.38 1.88 1.11 0.91 0.82 0.58 0.44 0.35 0.19 0.05 0.04 0.04
  • Sketch a scree plot.
  • Determine the minimum number of principal components needed to explain 90% of the total variation.
  • Determine the number of principal components whose eigenvalues are above average.
  1. Measurements are taken on \(p=3\) variables \(x_1\), \(x_2\) and \(x_3\), with sample correlation matrix \[ \mathbf R= \begin{pmatrix} 1 & 0.5792 & 0.2414 \\ 0.5792 & 1 & 0.5816 \\ 0.2414 & 0.5816 & 1 \end{pmatrix}. \] The variable \(z_j\) is the standardised versions of \(x_j\), \(j=1,2,3\), i.e. each \(z_j\) has sample mean \(0\) and variance \(1\). One observation has \(z_1 = z_2 = z_3 = 0\) and a second observation has \(z_1 = z_2 = z_3 =1\). Calculate the three principal component scores for each of these observations.

  2. Try exam question 1 part (a) from the 2017-18 exam paper.

  1. Try this tricky question from the 2016 exam.