graph signal processing - basics on fourier analysis
TRANSCRIPT
![Page 1: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/1.jpg)
Graph Signal Processing - Basics on FourierAnalysis
Prof. Luis Gustavo Nonato
August 23, 2017
![Page 2: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/2.jpg)
Inner Product
Given two vectors x = (x1, . . . , xd) and y = (y1, . . . , yd) in Rd,the dot product (or inner product) of x and y is
< x, y >=d
∑i=1
xiyi
If x and y are in Cd (complex vectors) then
< x, y >=d
∑i=1
xiyi
where yi is the complex conjugate of yi.The purpose of the conjugate is to ensure that the length ofz ∈ Cd is real and nonnegative (z = x + iy, zz = x2 + y2).
![Page 3: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/3.jpg)
Inner Product
Given two vectors x = (x1, . . . , xd) and y = (y1, . . . , yd) in Rd,the dot product (or inner product) of x and y is
< x, y >=d
∑i=1
xiyi
If x and y are in Cd (complex vectors) then
< x, y >=d
∑i=1
xiyi
where yi is the complex conjugate of yi.The purpose of the conjugate is to ensure that the length ofz ∈ Cd is real and nonnegative (z = x + iy, zz = x2 + y2).
![Page 4: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/4.jpg)
Inner Product
Given two vectors x = (x1, . . . , xd) and y = (y1, . . . , yd) in Rd,the dot product (or inner product) of x and y is
< x, y >=d
∑i=1
xiyi
If x and y are in Cd (complex vectors) then
< x, y >=d
∑i=1
xiyi
where yi is the complex conjugate of yi.
The purpose of the conjugate is to ensure that the length ofz ∈ Cd is real and nonnegative (z = x + iy, zz = x2 + y2).
![Page 5: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/5.jpg)
Inner Product
Given two vectors x = (x1, . . . , xd) and y = (y1, . . . , yd) in Rd,the dot product (or inner product) of x and y is
< x, y >=d
∑i=1
xiyi
If x and y are in Cd (complex vectors) then
< x, y >=d
∑i=1
xiyi
where yi is the complex conjugate of yi.The purpose of the conjugate is to ensure that the length ofz ∈ Cd is real and nonnegative (z = x + iy, zz = x2 + y2).
![Page 6: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/6.jpg)
Inner Product
Let f and g be functions defined on the square integrable spaceL2[a, b] (f : [a, b]→ C,
∫ ba |f (t)|
2dt < ∞).
The dot product of f and g is
< f , g >=∫ b
af (t)g(t)dt
The functions f and g are said orthogonal if < f , g >= 0A collection of functions ei are orthonormal if < ei, ej >= δij
Suppose E = {e1, . . . , en} a set of orthonormal functions basis.Then E generates a subspace of L2[a, b] and any function h inthis subspace can be written as (h lies in the span of E )
h =n
∑i=1
< h, ei > ei
![Page 7: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/7.jpg)
Inner Product
Let f and g be functions defined on the square integrable spaceL2[a, b] (f : [a, b]→ C,
∫ ba |f (t)|
2dt < ∞).The dot product of f and g is
< f , g >=∫ b
af (t)g(t)dt
The functions f and g are said orthogonal if < f , g >= 0A collection of functions ei are orthonormal if < ei, ej >= δij
Suppose E = {e1, . . . , en} a set of orthonormal functions basis.Then E generates a subspace of L2[a, b] and any function h inthis subspace can be written as (h lies in the span of E )
h =n
∑i=1
< h, ei > ei
![Page 8: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/8.jpg)
Inner Product
Let f and g be functions defined on the square integrable spaceL2[a, b] (f : [a, b]→ C,
∫ ba |f (t)|
2dt < ∞).The dot product of f and g is
< f , g >=∫ b
af (t)g(t)dt
The functions f and g are said orthogonal if < f , g >= 0
A collection of functions ei are orthonormal if < ei, ej >= δij
Suppose E = {e1, . . . , en} a set of orthonormal functions basis.Then E generates a subspace of L2[a, b] and any function h inthis subspace can be written as (h lies in the span of E )
h =n
∑i=1
< h, ei > ei
![Page 9: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/9.jpg)
Inner Product
Let f and g be functions defined on the square integrable spaceL2[a, b] (f : [a, b]→ C,
∫ ba |f (t)|
2dt < ∞).The dot product of f and g is
< f , g >=∫ b
af (t)g(t)dt
The functions f and g are said orthogonal if < f , g >= 0A collection of functions ei are orthonormal if < ei, ej >= δij
Suppose E = {e1, . . . , en} a set of orthonormal functions basis.Then E generates a subspace of L2[a, b] and any function h inthis subspace can be written as (h lies in the span of E )
h =n
∑i=1
< h, ei > ei
![Page 10: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/10.jpg)
Inner Product
Let f and g be functions defined on the square integrable spaceL2[a, b] (f : [a, b]→ C,
∫ ba |f (t)|
2dt < ∞).The dot product of f and g is
< f , g >=∫ b
af (t)g(t)dt
The functions f and g are said orthogonal if < f , g >= 0A collection of functions ei are orthonormal if < ei, ej >= δij
Suppose E = {e1, . . . , en} a set of orthonormal functions basis.Then E generates a subspace of L2[a, b] and any function h inthis subspace can be written as (h lies in the span of E )
h =n
∑i=1
< h, ei > ei
![Page 11: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/11.jpg)
Fourier Basis
Consider the Taylor expansion of the exponential function
ex = 1 + x +x2
2!+
x3
3!+
x4
4!+ · · ·
making x = it (complex number)
eit = 1 + (it) +(it)2
2!+
(it)3
3!+
(it)4
4!+ · · ·
eit =(
1− t2
2!+
t4
4!+ · · ·
)+ i
(t− t3
3!+
t5
5!− · · ·
)eit = cos(t) + i sin(t)
![Page 12: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/12.jpg)
Fourier Basis
Consider the Taylor expansion of the exponential function
ex = 1 + x +x2
2!+
x3
3!+
x4
4!+ · · ·
making x = it (complex number)
eit = 1 + (it) +(it)2
2!+
(it)3
3!+
(it)4
4!+ · · ·
eit =(
1− t2
2!+
t4
4!+ · · ·
)+ i
(t− t3
3!+
t5
5!− · · ·
)eit = cos(t) + i sin(t)
![Page 13: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/13.jpg)
Fourier Basis
Consider the Taylor expansion of the exponential function
ex = 1 + x +x2
2!+
x3
3!+
x4
4!+ · · ·
making x = it (complex number)
eit = 1 + (it) +(it)2
2!+
(it)3
3!+
(it)4
4!+ · · ·
eit =(
1− t2
2!+
t4
4!+ · · ·
)+ i
(t− t3
3!+
t5
5!− · · ·
)
eit = cos(t) + i sin(t)
![Page 14: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/14.jpg)
Fourier Basis
Consider the Taylor expansion of the exponential function
ex = 1 + x +x2
2!+
x3
3!+
x4
4!+ · · ·
making x = it (complex number)
eit = 1 + (it) +(it)2
2!+
(it)3
3!+
(it)4
4!+ · · ·
eit =(
1− t2
2!+
t4
4!+ · · ·
)+ i
(t− t3
3!+
t5
5!− · · ·
)eit = cos(t) + i sin(t)
![Page 15: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/15.jpg)
Fourier Basis
real(e−ix) real(e−2(ix)) real(e−3(ix))
One-parameter family of (Fourier) basis:
e−iλx
![Page 16: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/16.jpg)
Fourier Basis
real(e−ix) real(e−2(ix)) real(e−3(ix))
One-parameter family of (Fourier) basis:
e−iλx
![Page 17: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/17.jpg)
Fourier Transform
F(λ) =∫ ∞
−∞f (t) e−(iλt)dt
Fourier Transform
![Page 18: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/18.jpg)
Fourier Transform
F(λ) =∫ ∞
−∞f (t) e−(iλt)dt
Fourier Transform
f (x) =1√2π
∫ ∞
−∞F(λ)e(iλx)dλ
Inverse Fourier Transform
![Page 19: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/19.jpg)
Fourier Transform
F(λ) =∫ ∞
−∞f (t) e−(iλt)dt
Fourier Transform
f (x) =1√2π
∫ ∞
−∞F(λ)e(iλx)dλ
Inverse Fourier Transform
We will adopt the notation
F [f ] = F F−1[F] = f
![Page 20: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/20.jpg)
Fourier Transform
F(λ) =∫ ∞
−∞f (t) e−(iλt)dt
Fourier Transform
f (x) =1√2π
∫ ∞
−∞F(λ)e(iλx)dλ
Inverse Fourier Transform
We will adopt the notation
F [f ] = F F−1[F] = f
This notation makes clear that F : L2 → L2.
![Page 21: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/21.jpg)
Fourier Transform
cos(2π(3x))e−πx2
![Page 22: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/22.jpg)
Fourier Transform
f (x) = cos(2π(3x))e−πx2 F [f ]
![Page 23: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/23.jpg)
Fourier Transform: Properties
F and its inverse are linear operators
F [bf + cg] = bF [f ] + cF [g]
Fourier transform of a translation
F [f (x− c)](λ) = e−iλcF [f ](λ)
Fourier transform of a rescaling
F [f (cx)](λ) =1c
e−iλcF [f ](λ
c)
![Page 24: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/24.jpg)
Fourier Transform: Properties
F and its inverse are linear operators
F [bf + cg] = bF [f ] + cF [g]
Fourier transform of a translation
F [f (x− c)](λ) = e−iλcF [f ](λ)
Fourier transform of a rescaling
F [f (cx)](λ) =1c
e−iλcF [f ](λ
c)
![Page 25: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/25.jpg)
Fourier Transform: Properties
F and its inverse are linear operators
F [bf + cg] = bF [f ] + cF [g]
Fourier transform of a translation
F [f (x− c)](λ) = e−iλcF [f ](λ)
Fourier transform of a rescaling
F [f (cx)](λ) =1c
e−iλcF [f ](λ
c)
![Page 26: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/26.jpg)
Fourier Transform: Properties
F and its inverse are linear operators
F [bf + cg] = bF [f ] + cF [g]
Fourier transform of a translation
F [f (x− c)](λ) = e−iλcF [f ](λ)
Fourier transform of a rescaling
F [f (cx)](λ) =1c
e−iλcF [f ](λ
c)
![Page 27: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/27.jpg)
Fourier Transform: Properties
The convolution of two functions f and g is defined as
(f ∗ g)(x) =∫ ∞
−∞f (x− t)g(t)dt
Fourier transform of a convolution
F [f ∗ g] =√
2πF [f ]F [g]
Fourier transform of a product
F [f · g] =√
2πF [f ] ∗ F [g]
Perseval’s equation
‖F [f ]‖ = ‖f‖
![Page 28: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/28.jpg)
Fourier Transform: Properties
The convolution of two functions f and g is defined as
(f ∗ g)(x) =∫ ∞
−∞f (x− t)g(t)dt
Fourier transform of a convolution
F [f ∗ g] =√
2πF [f ]F [g]
Fourier transform of a product
F [f · g] =√
2πF [f ] ∗ F [g]
Perseval’s equation
‖F [f ]‖ = ‖f‖
![Page 29: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/29.jpg)
Fourier Transform: Properties
The convolution of two functions f and g is defined as
(f ∗ g)(x) =∫ ∞
−∞f (x− t)g(t)dt
Fourier transform of a convolution
F [f ∗ g] =√
2πF [f ]F [g]
Fourier transform of a product
F [f · g] =√
2πF [f ] ∗ F [g]
Perseval’s equation
‖F [f ]‖ = ‖f‖
![Page 30: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/30.jpg)
Fourier Transform: Properties
The convolution of two functions f and g is defined as
(f ∗ g)(x) =∫ ∞
−∞f (x− t)g(t)dt
Fourier transform of a convolution
F [f ∗ g] =√
2πF [f ]F [g]
Fourier transform of a product
F [f · g] =√
2πF [f ] ∗ F [g]
Perseval’s equation
‖F [f ]‖ = ‖f‖
![Page 31: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/31.jpg)
Fourier Transform: Properties
Impossibility of Perfect Band-Limiting
A consequence of the Paley–Wiener theorem is that a signal f (t)has perfectly band-limited Fourier transform F [f ], that is,
|F [f ](λ)| = 0 for λ > B
if only if for any T ∈ (−∞, ∞), there exists a t0 < T sucht thatf (t0) 6= 0.
In other words, a signal has perfectly bandlimited spectrum ifand only if the signal persists for all time.
![Page 32: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/32.jpg)
Fourier Transform: Properties
Impossibility of Perfect Band-Limiting
A consequence of the Paley–Wiener theorem is that a signal f (t)has perfectly band-limited Fourier transform F [f ], that is,
|F [f ](λ)| = 0 for λ > B
if only if for any T ∈ (−∞, ∞), there exists a t0 < T sucht thatf (t0) 6= 0.
In other words, a signal has perfectly bandlimited spectrum ifand only if the signal persists for all time.
![Page 33: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/33.jpg)
Linear Filters
A filter Lmaps a signal f into another signal L[f ].
A filter is called linear if L[bf + cg[= bL[f ] + cL[g]
A filter is time invariant if L[f (x− c)](t) = L[f (x)](t− c)
TheoremLet L be a linear and time invariant filter.There exists a function h such that
L[f ] = f ∗ h
PS. h is called the impulse response function of the filter
F [L[f ]] = F [f ]F [h]
![Page 34: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/34.jpg)
Linear Filters
A filter Lmaps a signal f into another signal L[f ].
A filter is called linear if L[bf + cg[= bL[f ] + cL[g]
A filter is time invariant if L[f (x− c)](t) = L[f (x)](t− c)
TheoremLet L be a linear and time invariant filter.There exists a function h such that
L[f ] = f ∗ h
PS. h is called the impulse response function of the filter
F [L[f ]] = F [f ]F [h]
![Page 35: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/35.jpg)
Linear Filters
A filter Lmaps a signal f into another signal L[f ].
A filter is called linear if L[bf + cg[= bL[f ] + cL[g]
A filter is time invariant if L[f (x− c)](t) = L[f (x)](t− c)
TheoremLet L be a linear and time invariant filter.There exists a function h such that
L[f ] = f ∗ h
PS. h is called the impulse response function of the filter
F [L[f ]] = F [f ]F [h]
![Page 36: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/36.jpg)
Linear Filters
A filter Lmaps a signal f into another signal L[f ].
A filter is called linear if L[bf + cg[= bL[f ] + cL[g]
A filter is time invariant if L[f (x− c)](t) = L[f (x)](t− c)
TheoremLet L be a linear and time invariant filter.There exists a function h such that
L[f ] = f ∗ h
PS. h is called the impulse response function of the filter
F [L[f ]] = F [f ]F [h]
![Page 37: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/37.jpg)
Linear Filters
A filter Lmaps a signal f into another signal L[f ].
A filter is called linear if L[bf + cg[= bL[f ] + cL[g]
A filter is time invariant if L[f (x− c)](t) = L[f (x)](t− c)
TheoremLet L be a linear and time invariant filter.There exists a function h such that
L[f ] = f ∗ h
PS. h is called the impulse response function of the filter
F [L[f ]] = F [f ]F [h]
![Page 38: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/38.jpg)
Linear Filters
L[f ] = f ∗ h → F [L[f ]] = F [f ] · F [h]
A filter can easily be understood in the frequency domain.High-, low-, and band-pass filters are easily designed.
![Page 39: Graph Signal Processing - Basics on Fourier Analysis](https://reader034.vdocument.in/reader034/viewer/2022042702/6265cd8644491930ab610d58/html5/thumbnails/39.jpg)
Linear Filters
L[f ] = f ∗ h → F [L[f ]] = F [f ] · F [h]
A filter can easily be understood in the frequency domain.High-, low-, and band-pass filters are easily designed.