This document outlines a teaching activity to introduce students to principal component analysis (PCA). It involves having students implement PCA step-by-step on the MNIST handwritten digit dataset to visualize and cluster the images. The activity aims to help students build an intuitive understanding of linear algebra operations and their connection to neuronal models and brain function. Specifically, students will reshape and center the data, construct the covariance matrix, perform singular value decomposition to obtain eigenvalues and eigenvectors, and project the data onto the resulting eigenvector basis.

1. A principled way to principal
components analysis

2. Teaching activity objectives
• Visualize large data sets.
• Transform the data to aid in this
visualization.
• Clustering data.
• Implement basic linear algebra operations.
• Connect this operations to neuronal
models and brain function.

3. Context for the activity
• Homework Assignment in 9.40 Intro to
neural Computation (Sophomore/Junior).
• In-class activity 9.014 Quantitative
Methods and Computational Models in
Neuroscience (1st year PhD).

4. Data visualization and
performing pca:

5. MNIST data set
28 by 28 pixels
8-bit gray scale images
These images live in
a 784 dimensional space
http://yann.lecun.com/exdb/mnist/

6. Can we cluster images in the
pixel space?

7. One possible visualization
There are more than 300000 possible pairwise pixel plots!!!

8. Is there a more principled way?
• Represent the data in a new basis set.
• Aids in visualization and potentially in
clustering and dimensionality reduction.
• PCA provides such a basis set by looking
at directions that capture most variance.
• The directions are ranked by decreasing
variance.
• It diagonalizes the covariance matrix.

9. Pedagogical approach
• Guide them step by step to implement PCA.
• Emphasize visualizations and geometrical
approach/intuition.
• We don’t use the MATLAB canned function
for PCA.
• We want students to get their hands “dirty”.
This helps build confidence and deep
understanding.

10. PCA Mantra
• Reshape the data to proper format for PCA.
• Center the data performing mean subtraction.
• Construct the data covariance matrix.
• Perform SVD to obtain the eigenvalues and
eigenvectors of the covariance matrix.
• Compute the variance explained per component
and plot it.
• Reshape the eigenvectors and visualize their
images.
• Project the mean subtracted data onto the
eigenvectors basis.

11. First 9 Eigenvectors

12. Projections onto the first 2 axes
• The first two PCs capture ~37% of the variance.
• The data forms clear clusters that are almost linearly separable

13. Building models: Synapses and
PCA

14. • 1949 book: 'The Organization
of Behavior' Theory about the
neural bases of learning
• Learning takes place at
synapses.
• Synapses get modified, they
get stronger when the pre- and
post- synaptic cells fire
together.
• "Cells that fire together, wire
together"
Hebbian Learning
Donald Hebb

15. Unstable
Building Hebbian synapses

16. Erkki Oja
Oja’s rule
A simplified neuron model as a principal component analyzer. Journal of Mathematical Biology,
15:267-273 (1982).
Feedback,forgetting term or regularizer
• Stabilizes the Hebbian rule.
• Leads to a covariance learning rule: the weights
converge to the first eigenvector of the covariance
matrix.
• Similar to power iteration method.

17. Learning outcomes
• Visualize and manipulate a relatively large and
complex data set.
• Perform PCA by building it step by step.
• Gain an intuition of the geometry involved in a
change of basis and projections.
• Start thinking about basic clustering
algorithms.
• Discuss on dimensionality reduction and other
PCA applications

18. Learning outcomes (cont)
• Discuss the assumptions, limitations and
shortcomings of applying PCA in different
contexts.
• Build a model of how PCA might actually
take place in neural circuits.
• Follow up: eigenfaces, is the brain doing
PCA to recognize faces?