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Matlab代写 | CSE 5523. Homework 2

Matlab代写 | CSE 5523. Homework 2

该Homework是使用matlab svm进行梯度下降分析

CSE 5523. Homework 2. Due Nov 22th in class.
Problem 1. Run a linear SVM on the two class dataset given online (you
can use a standard toolbox). Compare its performance to that of the least
squares linear classifier.
Instructions: download 79.mat, which contains images of digits. Each
image is given as a 28×28 matrix of grayscale pixel values. It is stored as a
784 (= 28×28) array. You are given 1000 images of 7 and 1000 images of 9.
These are stored as a single 2000 × 784 matrix in the file 79.mat. The first
1000 digits are sevens, the rest are nines. Download that file and type ”load
79.mat” in Matlab. The matrix d79 contains the data. You can visualize
the digits by typing, e.g., the following:
colormap(gray);
x = reshape (d79(1234,:),28,28);
y = x(:,28:-1:1);
pcolor(y)
This bit of code shows you the digit number 1234 (which is a 9).
Problem 2.
Implement (do not use standard toolboxes) the Least Squares classifier
using gradient descent. Compare your results to standard least squares
classifier (obtained using pseudo-inverse).
Problem 3. Reduce the dimension of the dataset (both train and test)
to 400 using the Principal Components Analysis (we have not discussed
it yet but you can use a standard toolbox). Apply linear regression and
SVM (using large value of the parameter C) to 50, 100,150,…2000 training
examples (i.e., 25, 50, . . . , 1000 from each class, you can choose them at
random). Plot the error on the test set. Observations?
Problem 4. Use gradient descent (instead of the explicit solution) for linear
regression in Problem 3. For 50, 200, 400, 1000 and 2000 training examples
plot the dependence of the test error on the number of iterations. What do
you observe?
Problem 5. Implement a kernel machine with Gaussian kernel (choose the
bandwdith by appropriate cross-validation). You can train it to have loss
zero. Specifically, construct a kernel matrix K (Kij = k(xi
, xj )) and find
the coefficients by the formula α = K−1y, where y is the (column) vector of
labels. The final classifier has the form Pαik(xi
, x). Apply it to the digit
data and report the results.
Problem 6. Apply Random Fourier Feature embedding to the digit dataset
and train using linear regression. Plot the dependence of the test error on
1
the number of random features (use multiples of 500, from 500 to 6000).
2

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