We can also find basis
vectors for vector
spaces other than
Rn
n
.
Let
PnPn
be the vector space of n-th order polynomials on (-1, 1) with
real coefficients (verify
P2P2
is a v.s. at home).
P2P2
= {all quadratic polynomials}. Let
b0
t=1
b0
t
1
,
b1
t=t
b1
t
t
,
b2
t=t2
b2
t
t
2
.
b0
t
b1
t
b2
t
b0
t
b1
t
b2
t
span
P2P2,
i.e. you can write any
ft∈
P2
f
t
P2
as
ft=
α0
b0
t+
α1
b1
t+
α2
b2
t
f
t
α0
b0
t
α1
b1
t
α2
b2
t
for some
αi
∈R
αi
.
P2P2
is 3 dimensional.
ft=t2−3t−4
f
t
t
2
3
t
4
Alternate basis
b0
t
b1
t
b2
t=1t12(3t2−1)
b0
t
b1
t
b2
t
1
t
1
2
3
t
2
1
write
ft
f
t
in terms of this new basis
d0
t=
b0
t
d0
t
b0
t
,
d1
t=
b1
t
d1
t
b1
t
,
d2
t=32
b2
t−12
b0
t
d2
t
3
2
b2
t
1
2
b0
t
.
ft=t2−3t−4=4
b0
t−3
b1
t+
b2
t
f
t
t
2
3
t
4
4
b0
t
3
b1
t
b2
t
ft=
β0
d0
t+
β1
d1
t+
β2
d2
t=
β0
b0
t+
β1
b1
t+
β2
(32
b2
t−12
b0
t)
f
t
β0
d0
t
β1
d1
t
β2
d2
t
β0
b0
t
β1
b1
t
β2
3
2
b2
t
1
2
b0
t
ft=
β0
b0
t+
β1
b1
t+32
β2
b2
t
f
t
β0
1
2
b0
t
β1
b1
t
3
2
β2
b2
t
so
β0
−12=4
β0
1
2
4
β1
=-3
β1
-3
32
β2
=1
3
2
β2
1
then we get
ft=4.5
d0
t−3
d1
t+23
d2
t
f
t
4.5
d0
t
3
d1
t
2
3
d2
t
ei
ω0
nt|n=−∞∞
n
ω0
n
t
is a basis for
L2
0T
L2
0
T
,
T=2π
ω0
T
2
ω0
,
ft=∑n
Cn
ei
ω0
nt
f
t
n
Cn
ω0
n
t
.
We calculate the expansion coefficients with
Cn
=1T∫0T(fte−(i
ω0
nt))dt
Cn
1
T
t
0
T
f
t
ω0
n
t
(1)
There are an infinite number of elements in
the basis set, that means
L2
0T
L2
0
T
is infinite dimensional (scary!).
Infinite-dimensional spaces are hard to
visualize. We can get a handle on the intuition by recognizing
they share many of the same mathematical properties with
finite dimensional spaces. Many concepts apply to both (like
"basis expansion"). Some don't (change of basis isn't a nice
matrix formula).
"My introduction to signal processing course at Rice University."