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Assignment 1 – Dimensionality Reduction

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CS 383 – Machine Learning
Assignment 1 – Dimensionality Reduction
Introduction
In this assignment, in addition to related theory/math questions, you’ll work on visualizing data and
reducing its dimensionality.
You may not use any functions from machine learning library in your code, however you may use
statistical functions. For example, if available you MAY NOT use functions like
• pca
• entropy
however you MAY use basic statistical functions like:
• std
• mean
• cov
• eig
Grading
Although all assignments will be weighed equally in computing your homework grade, below is the
grading rubric we will use for this assignment:
Part 1 (Theory) 30pts
Part 2 (PCA) 40pts
Part 3 (Eigenfaces) 20pts
Report 10pts
TOTAL 100pts
Table 1: Grading Rubric
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DataSets
Yale Faces Datasaet This dataset consists of 154 images (each of which is 243×320 pixels) taken
from 14 people at 11 different viewing conditions (for our purposes, the first person was removed
from the official dataset so person ID=2 is the first person).
The filename of each images encode class information:
subject< ID >.< condition >
Data obtained from: http://cvc.cs.yale.edu/cvc/projects/yalefaces/yalefaces.html
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1 Theory Questions
1. (15 points) Consider the following data:
Class 1 =






−2 1
−5 −4
−3 1
0 3
−8 11






, Class 2 =






−2 5
1 0
5 −1
−1 −3
6 1






(a) Compute the information gain for each feature. You could standardize the data overall,
although it won’t make a difference. (13pts).
(b) Which feature is more discriminating based on results in Part (a) (2pt)?
2. (15 points) In principle component analysis (PCA) we are trying to maximize the variance of
the data after projection while minimizing how far the magnitude of w, |w| is from being unit
length. This results in attempting to find the value of w that maximizes the equation
w
TΣw − α(w
Tw − 1)
where Σ is the covariance matrix of the observable data matrix X.
One problem with PCA is that it doesn’t take class labels into account. Therefore projecting
using PCA can result in worse class separation, making the classification problem more difficult,
especially for linear classifiers.
To avoid this, if we have class information, one idea is to separate the data by class and aim to
find the projection that maximize the distance between the means of the class data after projection, while minimizing their variance after projection. This is called linear discriminant
analysis (LDA).
Let Ci be the set of observations that have class label i, and µi
, σi be the mean and standard
deviations, respectively, of those sets. Assuming that we only have two classes, we then want
to find the value of w that maximizes the equation:
(µ1w − µ2w)
T
(µ1w − µ2w) − λ((σ1w)
T
(σ1w) + (σ2w)
T
(σ2w))
Which is equivalent to
w
T
(µ1 − µ2)
T
(µ1 − µ2)w − λ(w
T

T
1 σ1 + σ
T
2 σ2)w)
Show that to maximize we must solve an eigen-decomposition problem, i.e Aw = bw.
In particular what are A and b for this equation.
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2 (40pts) Dimensionality Reduction via PCA
Download and extract the dataset yalefaces.zip from Blackboard. This dataset has 154 images
(N = 154) each of which is a 243×320 image (D = 77760). In order to process this data your script
will need to:
1. Read in the list of files
2. Create a 154×1600 data matrix such that for each image file
(a) Read in the image as a 2D array (234×320 pixels)
(b) Subsample the image to become a 40×40 pixel image (for processing speed)
(c) Flatten the image to a 1D array (1×1600)
(d) Concatenate this as a row of your data matrix.
Once you have your data matrix, your script should:
1. Standardizes the data
2. Reduces the data to 2D using PCA
3. Graphs the data for visualization
Recall that although you may not use any package ML functions like pca, you may use statistical
functions like eig.
Your graph should end up looking similar to Figure 1 (although it may be rotated differently, depending how you ordered things).
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Figure 1: 2D PCA Projection of data
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3 (20 points) Eigenfaces
Download and extract the dataset yalefaces.zip from Blackboard. This dataset has 154 images
(N = 154) each of which is a 243×320 image (D = 77760). In order to process this data your script
will need to:
1. Read in the list of files
2. Create a 154×1600 data matrix such that for each image file
(a) Read in the image as a 2D array (234×320 pixels)
(b) Subsample the image to become a 40×40 pixel image (for processing speed)
(c) Flatten the image to a 1D array (1×1600)
(d) Concatenate this as a row of your data matrix.
Write a script that:
1. Imports the data as mentioned above.
2. Standardizes the data.
3. Performs PCA on the data (again, although you may not use any package ML functions like
pca, you may use statistical functions like eig).
4. Determines the number of principle components necessary to encode at least 95% of the information, k.
5. Visualizes the most important principle component as a 40×40 image (see Figure 2).
6. Reconstructs the first person using the primary principle component and then using the k most
significant eigen-vectors (see Figure 3). For the fun of it maybe even look to see if you can
perfectly reconstruct the face if you use all the eigen-vectors!
Your principle eigenface should end up looking similar to Figure 2.
Figure 2: Primary Principle Component
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Your reconstruction should end up looking similar to Figure 3.
Figure 3: Reconstruction of first person (ID=2)
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Submission
For your submission, upload to Blackboard a single zip file containing:
1. PDF Writeup
2. Source Code
3. readme.txt file
The readme.txt file should contain information on how to run your code to reproduce results for
each part of the assignment. Do not include spaces or special characters (other than the underscore
character) in your file and directory names. Doing so may break our grading scripts.
The PDF document should contain the following:
1. Part 1: Your answers to the theory questions.
2. Part 2: The visualization of the PCA result
3. Part 3:
(a) Number of principle components needed to represent 95% of information, k.
(b) Visualization of primary principle component
(c) Visualization of the reconstruction of the first person using
i. Original image
ii. Single principle component
iii. k principle components.
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