Linear Fitting Revisited
Denote each row of D as d
⊤
i
. Then,
E =
n
X
i=1
(d
⊤
i
a − v
i
)
2
= kD a − vk
2
. (6)
So, linear least squares problem can be described very compactly as
min
a
kD a − vk
2
. (7)
To show that the solution in Eq. 5 minimizes error E, need to
differentiate E with respect to a and set it to zero:
dE
da
= 0. (8)
How to do this differentiation?
Leow Wee Khen g (NUS) Matrix Differentiation 4 / 34
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