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# Data Analytics Homework #3

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Engineering Applications of Machine Learning and
Data Analytics
Homework #3

Instructions: There are four problems. X Partial credit is given for answers that are partially
correct. No credit is given for answers that are wrong or illegible. Write neatly.
You must submit two PDFs on D2L. The first PDF has the results to the analytical questions
as well as figures that are generated
Problem 1: Problem 2:

Category:
5/5 - (2 votes)

Engineering Applications of Machine Learning and
Data Analytics
Homework #3

Instructions: There are four problems. X Partial credit is given for answers that are partially
correct. No credit is given for answers that are wrong or illegible. Write neatly.
You must submit two PDFs on D2L. The first PDF has the results to the analytical questions
as well as figures that are generated
Problem 1: Problem 2:
Problem 3: :
Total:
arizona.edu 1 March 04, 2022
Spring 2021 University of Arizona
1 The `2 Support Vector Machine [20pts]
In class, we discussed that if our data is not linearly separable, then we need to modify our optimization problem to include slack variables. The formulation that was used is known as the `1-norm
soft margin SVM. Now consider the formulation of the `2-norm soft margin SVM, which squares the
slack variables within the sum. Notice that non-negativity of the slack variables has been removed.
arg min
w,b,ξ
1
2
kwk
2 +
C
2
Xn
i=1
ξ
2
i
s.t. yi(wT xi + b) ≥ 1 − ξi ∀i ∈ [n]
Derive the dual form expression along with any constraints. Work must be shown. Hints: Refer to
the methodology that was used in class to derive the dual form. The solution is given by:
arg max
α
Xn
i=1
αi −
1
2
Xn
i=1
Xn
j=1
αiαjyiyjx
T
i xj −
1
2C
Xn
i=1
α
2
i
s.t. αi ≥ 0 ∀i ∈ [n] and Xn
i=1
αiyi = 0
arizona.edu 2 March 04, 2022
Spring 2021 University of Arizona
2 Domain Adaptation Support Vector Machines [20pts]
We now look at a different type of SVM that is designed for domain adaptation and optimizes
the hyperplanes given by wS (source hyperplane) before optimizing wT (target hyperplane). The
process begins by training a support vector machine on source data then once data from the target
are available, train a new SVM using the hyperplane from the first SVM and the data from the
target to solve for a new “domain adaptation” SVM.
The primal optimization problem is given by
arg min
wT ,ξ
1
2
kwT k
2 + C
Xn
i=1
ξi − BwT
T wS
s.t. yi(wT
T xi + b) ≥ 1 − ξi ∀i ∈ {1, . . . , n}
ξi ≥ 0 ∀i ∈ {1, . . . , n}
where wS is hyperplane trained on the source data (assumed to be known), wT is hyperplane for
the target, yi ∈ {±1} is the label for instance xi
, C & B are regularization parameters defined
by the user and ξi
is a slack variable for instance xi
. The problem becomes finding a hyperplane,
wT , that minimizes the above objective function subject to the constraints. Solve/derive the dual
optimization problem.
Note: I will give the class the solution to this problem prior to the due date because Problem #3
requires that you implement this algorithm in code.
arizona.edu 3 March 04, 2022
Spring 2021 University of Arizona
3 Domain Adaptation SVM (Code) [20pts]
Implement the domain adaptation SVM from Problem #2. A data setfor the source and target
domains (both training and testing) have been uploaded to D2L. There are several ways to implement this algorithm. If I were doing this for an assignment, I would implement the SVM (both the
domain adaptation SVM and normal SVM) directly using quadratic programming. You do not need
to build the classifier (i.e., solve for the bias term); however, you will need to find wT and wS. To
find the weight vectors, you will need to solve a quadratic programming problem and look through
the documentation to learn how to solve this optiization task. The following Python packages are
recommended:
• CVXOPT(https://cvxopt.org/)
• PyCVX (https://www.cvxpy.org/install/)
Note: Your solution can (and should) use any of the packages above.
arizona.edu 4 March 04, 2022

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