Description
1. PCA experiments: Take any digit data ( ‘0’,…,‘9’), or all of them, from website
http://www-stat.stanford.edu/~tibs/ElemStatLearn/datasets/zip.digits/
and perform PCA experiments with Matlab or other languages (e.g. ipynb) you are familiar:
(a) Set up data matrix X = (x1, . . . , xn) ∈ R
p×n
;
(b) Compute the sample mean ˆµn and form X˜ = X − eµˆ
T
n
;
(c) Compute top k SVD of X˜ = USkV
T
;
(d) Plot eigenvalue curve, i.e. i vs. λi(Σˆ
n)/tr(Σˆ
n) (i = 1, . . . , k), with top-k eigenvalue λi
for sample covariance matrix Σˆ
n =
1
nX˜ ∗ X˜ T
, which gives you explained variation of
data by principal components;
(e) Use imshow to visualize the mean and top-k principle components as left singular vectors
U = [u1, . . . , uk];
(f) For k = 1, order the images (xi) (i = 1, . . . , n) according to the top first right singular
vector, v1, in an ascending order of v1(i);
(g) For k = 2, scatter plot (v1, v2) and select a grid on such a plane to show those images
on the grid (e.g. Figure 14.23 in book [ESL]: Elements of Statistical Learning).
(h)* You may try the parallel analysis with permutation test to see how many significant
principle components you will obtain.
2. MDS of cities: Go to the following website
http://www.geobytes.com/citydistancetool.htm
Perform the following experiment.
(a) Input a few cities (no less than 7) in your favorite, and collect the pairwise air traveling
distances shown on the website in to a matrix D;
(b) Make your own codes of Multidimensional Scaling algorithm for D;
(c) Plot the normalized eigenvalues λi/(
P
i
λi) in a descending order of magnitudes, analyze
your observations (did you see any negative eigenvalues? if yes, why?);
1
Homework 1. PCA and MDS 2
(d) Make a scatter plot of those cities using top 2 or 3 eigenvectors, and analyze your
observations.
3. Positive Semi-definiteness: Recall that a n-by-n real symmetric matrix K is called positive
semi-definite (p.s.d. or K 0) iff for every x ∈ R
n
, x
T Kx ≥ 0.
(a) Show that K 0 if and only if its eigenvalues are all nonnegative.
(b) Show that dij = Kii +Kjj −2Kij is a squared distance function, i.e. there exists vectors
ui
, vj ∈ R
n
(1 ≤ i, j ≤ n) such that dij = kui − ujk
2
.
(c) Let α ∈ R
n be a signed measure s.t. P
i αi = 1 (or e
Tα = 1) and Hα = I − eαT be the
Householder centering matrix. Show that Bα = −
1
2HαDHT
α 0 for matrix D = [dij ].
(d) If A 0 and B 0 (A, B ∈ R
n×n
), show that A + B = [Aij + Bij ]ij 0 (elementwise
sum), and A ◦ B = [AijBij ]ij 0 (Hadamard product or elementwise product).
4. Distance: Suppose that d : R
p × R
p → R is a distance function.
(a) Is d
2 a distance function? Prove or give a counter example.
(b) Is √
d a distance function? Prove or give a counter example.
5. ∗Singular Value Decomposition: The goal of this exercise is to refresh your memory about
the singular value decomposition and matrix norms. A good reference to the singular value
decomposition is Chapter 2 in this book:
Matrix Computations, Golub and Van Loan, 3rd edition.
(a) Existence: Prove the existence of the singular value decomposition. That is, show that if
A is an m×n real valued matrix, then A = UΣV
T
, where U is m×m orthogonal matrix,
V is n × n orthogonal matrix, and Σ = diag(σ1, σ2, . . . , σp) (where p = min{m, n}) is an
m × n diagonal matrix. It is customary to order the singular values in decreasing order:
σ1 ≥ σ2 ≥ . . . ≥ σp ≥ 0. Determine to what extent the SVD is unique. (See Theorem
2.5.2, page 70 in Golub and Van Loan).
(b) Best rank-k approximation – operator norm: Prove that the “best” rank-k approximation
of a matrix in the operator norm sense is given by its SVD. That is, if A = UΣV
T
is the
SVD of A, then Ak = UΣkV
T
(where Σk = diag(σ1, σ2, . . . , σk, 0, . . . , 0) is a diagonal
matrix containing the largest k singular values) is a rank-k matrix that satisfies
kA − Akk = min
rank(B)=k
kA − Bk.
(Recall that the operator norm of A is kAk = maxkxk=1 kAxk. See Theorem 2.5.3 (page
72) in Golub and Van Loan).
(c) Best rank-k approximation – Frobenius norm: Show that the SVD also provides the best
rank-k approximation for the Frobenius norm, that is, Ak = UΣkV
T
satisfies
kA − AkkF = min
rank(B)=k
kA − BkF .
Homework 1. PCA and MDS 3
(d) Schatten p-norms: A matrix norm k · k that satisfies
kQAZk = kAk,
for all Q and Z orthogonal matrices is called a unitarily invariant norm. The Schatten
p-norm of a matrix A is given by the `p norm (p ≥ 1) of its vector of singular values,
namely,
kAkp =
X
i
σ
p
i
!1/p
.
Show that the Schatten p-norm is unitarily invariant. Note that the case p = 1 is
sometimes called the nuclear norm of the matrix, the case p = 2 is the Frobenius norm,
and p = ∞ is the operator norm.
(e) Best rank-k approximation for unitarily invariant norms: Show that the SVD provides
the best rank-k approximation for any unitarily invariant norm. See also 7.4.51 and
7.4.52 in:
Matrix Analysis, Horn and Johnson, Cambridge University Press, 1985.
(f) Closest rotation: Given a square n × n matrix A whose SVD is A = UΣV
T
, show that
its closest (in the Frobenius norm) orthogonal matrix R (satisfying RRT = RT R = I)
is given by R = UV T
. That is, show that
kA − UV T
kF = min
RRT =RT R=I
kA − RkF ,
where A = UΣV
T
. In other words, R is obtained from the SVD of A by dropping the
diagonal matrix Σ. Use this observation to conclude what is the optimal rotation that
aligns two sets of points p1, p2, . . . , pn and q1, . . . , qn in R
d
P
, that is, find R that minimizes
n
i=1 kRpi − qik
2
. See also (the papers are posted on course website):
• [Arun87] Arun, K. S., Huang, T. S., and Blostein, S. D., “Least-squares fitting of two
3-D point sets”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 9
(5), pp. 698–700, 1987.
• [Keller75] Keller, J. B., “Closest Unitary, Orthogonal and Hermitian Operators to a
Given Operator”, Mathematics Magazine, 48 (4), pp. 192–197, 1975.
• [FanHoffman55] Fan, K. and Hoffman, A. J., “Some Metric Inequalities in the Space of
Matrices”, Proceedings of the American Mathematical Society, 6 (1), pp. 111–116, 1955.
