Description
0. Project (0 points)
Start thinking about the course project. There is nothing to turn in now, so the main thing
to do is to find a group of 3-4 people and start talking about possible topics. The project will
ask you to find a machine learning research paper, understand it, implement it and try it out,
and provide a critical evaluation of the strengths and weaknesses of the method. Optionally,
you can extend the method or develop your own for comparison. A typeset project report of
about 8 pages will be the final deliverable. More details to follow next week.
1. Vector Quantization (15 points)
Download the image mandrill.tiff from Canvas. In this problem you will apply the kmeans algorithm to image compression. In this context it is also known as the Lloyd-Max
algorithm.
a. (10 points) First, use the following code to partition the image into M × M blocks, and
reshape each block into a vector of dimension 3M2
. The 3 comes from the fact that this
is a color image, and so there are three intensities for each pixel. Assume that M, like
the image dimensions, is a power of 2.
In Matlab:
\begin{quote}
% load image
y = double(imread(’mandrill.tiff’));
% extract blocks
M = 2; % block side-length
n = prod(size(y))/(3*M*M); % number of blocks
d = size(y,1); % image length/width
c=0; % counter
x = zeros(n,3*M*M);
for i=1:M:d % loop through blocks
for j=1:M:d
c = c+1;
x(c,:) = reshape(y(i:i+M-1,j:j+M-1,:),[1,M*M*3]);
end
end
\end{quote}
In Python:
\begin{quote}
from PIL import Image
import numpy as np
#To display this image
im = Image.open(’mandrill.tiff’)
#im.show()
y = np.array(im)
M = 2 #block side-length
n = np.prod(y.shape)/(3*M*M) #number of blocks
d = y.shape[0]
c = 0 # counter
x = np.zeros([n,3*M*M])
for i in range(0,d,M):
for j in range(0,d,M):
#print c,i,j,M,y[i:i+M,j:j+M,:].shape, M*M*3
x[c,:] = np.reshape(y[i:i+M,j:j+M,:],[1,M*M*3])
c = c+1
\end{quote}
Next, write a program that will cluster these vectors using the k-means algorithm.
You should write the k-means algorithm yourself. The function dist2 from a previous
assignment can be used to eliminate all loops except for the outermost loop over the
iterations of k-means. This will make your program much more efficient. Use the min
(Matlab) or np.min (Python) command to determine the cluster assignments. To update
the centroids, the find (Matlab) or np.where (Python) command is useful. Please
initialize the cluster means to be randomly selected data points using the following
code:
In Matlab
rng(0);
perm = randperm(n);
m = x(perm(1:k),:); % initial cluster centers
In Python
np.random.seed(0)
perm = np.random.permutation(n)
m = x[perm[0:k,:]] # initial cluster centers
Finally, write a program to reconstruct a quantized version of the original image by
replacing each block in the original image by the nearest cluster center.
Deliverables: Test your code using M = 2 and k = 100. How many iterations does
the k-means algorithm require to converge? Hand in a plot of the objective function
value versus iteration. How does the compressed image look compared to the original?
What regions of the image are best preserved, and which are not? Hand in a plot of the
difference of the two images, using the command imagesc (Matlab) or plt.imshow()
(Python) to display the difference image. You may need to normalize the pixel intensities
in the difference image to get a good view (do not normalize your images for other parts
of the problem). Also report the compression ratio (use your formula from part b.).
Finally, report the relative mean absolute error of the compressed image, defined as
1
3N2
PN
i=1
PN
j=1
P3
r=1 |I(i, j, r) − I
0
(i, j, r)|
256
where I and I
0 are the original and compressed images viewed as 3-D arrays. This
quantity can be viewed as the average error in pixel intensity relative to the range of
pixel intensities. Finally, submit your code.
Programming tip: If C is a matrix or multi-dimensional array in Matlab, C(:) converts
C to a columnn vector (like using the reshape command).
b. (5 points) The original uncompressed image uses 24 bits per pixel (bpp), 8 bits for each
color. Assuming an image of size N × N, where N is a power of 2, what is the number
of bits per pixel, as a function of N, k, and M, needed to store the compressed image?
What is the compression ratio, which is defined as the ratio of bpp in the compressed
image relative to the original uncompressed image? Hint: To calculate the bpp of the
compressed image, imagine you need to transmit the compressed image to a friend who
knows the compression strategy.
2. EM Algorithm for Mixed Linear Regression (20 points)
Consider regression training data (x1, y1), . . . ,(xn, yn), iid realizations of (X, Y ) ∈ R
d×R
where the conditional distribution of Y given X is modeled by the pdf
f(y|x; θ) ∼
X
K
k=1
kφ(y; wT
k x + bk, σ2
k
),
where θ = (1, . . . , K, w1, . . . , wK, σ2
1
, . . . , σ2
K) be a list of the model parameters. Derive
an EM algorithm for maximizing the likelihood by following these steps.
a. (5 points) Denote x = (x1, . . . , xn) and y = (y1, . . . , yn). First, write down the formula for
the log-likelihood `(θ; y|x) = log f(y|x; θ) where f(y|x; θ) is the model (with parameters
θ) for y given x. Then, introduce an appropriate hidden variable and write down the
complete-data log-likelihood. Here and below, use notation consistent with the EM
algorithm for GMMs whenever possible. Finally, determine the E-step. Give an explicit
formula for Q(θ, θ
(j)
) in terms of θ, θ
(j)
, and the data.
c. (5 points) Determine the M-step. Suggestions: Use Lagrange multiplier theory to optimize
the weights k. To optimize (wk, bk, σ2
k
), first hold σ
2
k
fixed and find the optimal (wk, bk),
then plug that in and find the optimal σ
2
k
. Just treat σ
2
k
as a variable (not the square of
a variable).
d. (10 points) Now let’s put these ideas into practice. Generate training data using the
following Matlab code: In Matlab
clear all
close all
rng(0);
n = 200; % sample size
K = 2; % number of lines
e = [.7 .3]; % mixing weights
w = [-2 1]; % slopes of lines
b = [.5 -.5]; % offsets of lines
v = [.2 .1]; % variances
for i=1:n
x(i) = rand;
if rand < e(1);
y(i) = w(1)*x(i) + b(1) + randn*sqrt(v(1));
else
y(i) = w(2)*x(i) + b(2) + randn*sqrt(v(2));
end
end
plot(x,y,’bo’)
hold on
t=0:0.01:1;
plot(t,w(1)*t+b(1),’k’)
plot(t,w(2)*t+b(2),’k’)
In Python:
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(0)
n = 200 #sample size
K = 2 #number of lines
e = np.array([0.7,0.3]) #mixing weights
w = np.array([-2,1]) #slopes of lines
b = np.array([0.5,-0.5]) #offsets of lines
v = np.array([0.2,0.1]) #variances
x = np.zeros([n])
y = np.zeros([n])
for i in range(0,n):
x[i] = np.random.rand(1)
if np.random.rand(1) < e[0]:
y[i] = w[0]*x[i] + b[0] + np.random.randn(1)*np.sqrt(v[0])
else:
y[i] = w[1]*x[i] + b[1] + np.random.randn(1)*np.sqrt(v[1])
plt.plot(x,y,’bo’)
t = np.linspace(0, 1, num=100)
plt.plot(t,w[0]*t+b[0],’k’)
plt.plot(t,w[1]*t+b[1],’k’)
plt.show()
Implement and run the EM algorithm, and report or turn in the following:
• The number of iterations to reach convergence
• A plot of the log-likelihood as a function of iteration number
• The estimated model parameters
• A plot showing the data, true lines (solid), and estimated lines (dotted) together.
• Your code.
Comments:
• Initialize your variables as follows. In Matlab:
= [.5 .5]
= [1 -1];
= [0 0];
= repmat(var(y),1,2);
In Python
= np.array([.5 , .5])
= np.array([1 ,-1])
= np.array([0, 0])
= np.array([np.var(y),np.var(y)])
where you get to define the variable names, and the variables are listed in the same
order as above.
• Stop iterating when the increase in log-likelihood is less than 10−4
.
• In Matlab, use the command normpdf to evaluate the Gaussian pdf. Note that the
third argument is the standard devation, not the variance. In Python, use
from scipy.stats import norm
norm.pdf()
• This is optional, but it is helpful and interesting to plot the current estimate at each
iteration, and watch the estimate evolve. To update the plot while the program is
running, you can use drawnow in Matlab. The command clf will clear the current
plot. In Python look at plt.ion() and plt.clf(). For more information, see here
or here.
3. KL Divergence and KDE Model Selection (5 points)
If g and h are two densities, the KL divergence between them is defined as
D(g||h) := Z
log
g(x)
h(x)
g(x)dx.
We have shown that the KL divergence is nonnegative and equal to zero iff g = h (when
we showed the EM algorithm has the ascent property). Therefore it is a performance measure that could be used for selecting the bandwidth of a KDE. Devise a leave-one-out type
procedure for selecting the bandwidth that aims to minimize
D(f||fbσ)
where f is the true density.
SUPPLEMENTAL EXERCISES
Not to be turned in.
1. In the notes, it was shown that both the Eckart-Young theorem and the generalized Rayleigh
quotient theorem imply the PCA theorem.
Can you use the Eckhart-Young theorem to prove the GRQ theorem? Can you use the GRQ
theorem to prove the Eckart-Young theorem? Are the three theorems (including the PCA
theorem) equivalent (i.e., each one can be used to prove the others)?
3. Let C
∗
k
be the globally optimal k-means cluster map. Show that W(C
∗
k
) is nonincreasing as a
function of k.
3. Let C
(j)
k
be estimate of C
∗
k
produced by the j-th iteration of k-means. Show that W(C
(j)
k
) is
nonincreasing as a function of j.
4. In the image compression problem above, play around with M and k.
