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# Show that if the λk are positive, Using the Optdigits dataset from the UCI repository, implement PCA

INSTRUCTIONS TO CANDIDATES

1. Fisher’s linear discriminant is

wˆ T Sbwˆ

wˆ    = arg max J(wˆ ) = arg max wˆ T S   wˆ ,

w

where Sb = (m1 − m2)(m1 − m2)T and Sw = Σj Σα(xα − mj)(xα − mj)T .

(a) By writing ∂J

= 0, show that

S−w1Sbwˆ  = J(wˆ )wˆ .

(b) Explain why wˆ ∗  is the eigenvector for which J(wˆ ) is the maximum eigenvalue of S−w1Sb.

(c) Explain why Sbwˆ

is always in the direction of m1 − m2 and thus show that

wˆ ∗   =   const. • S−w1  (m1 − m2).

2. Another way to optimize Fisher’s linear discriminant (suggested by Barry Fridling):

(a) Show that for any two real vectors x and y

(xT y)2 ≤ (xT x)(yT y), (Cauchy-Schwarz).

(b) Show that if the λk are positive,

Using the Optdigits dataset from the UCI repository, implement PCA. Reconstruct the digit images  and  calculate  the  reconstruction  error  E(n) =      j  ||xˆj − x||2  for  various  values  of  n,  the number of eigenvectors. Plot E(n) versus n.

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