The document describes parametric density estimation using Gaussian mixture models and the EM algorithm. It introduces density estimation problems and defines the parametric density estimation problem as estimating the parameters of a mixture of Gaussian distributions that best fit sample data. The EM algorithm is used to find maximum likelihood estimates for the parameters by iteratively maximizing the likelihood function when data is incomplete. The document outlines introducing density estimation, Gaussian mixture models, presenting some results, and discussing other applications of the EM algorithm.
In this article we consider macrocanonical models for texture synthesis. In these models samples are generated given an input texture image and a set of features which should be matched in expectation. It is known that if the images are quantized, macrocanonical models are given by Gibbs measures, using the maximum entropy principle. We study conditions under which this result extends to real-valued images. If these conditions hold, finding a macrocanonical model amounts to minimizing a convex function and sampling from an associated Gibbs measure. We analyze an algorithm which alternates between sampling and minimizing. We present experiments with neural network features and study the drawbacks and advantages of using this sampling scheme.
In this article we consider macrocanonical models for texture synthesis. In these models samples are generated given an input texture image and a set of features which should be matched in expectation. It is known that if the images are quantized, macrocanonical models are given by Gibbs measures, using the maximum entropy principle. We study conditions under which this result extends to real-valued images. If these conditions hold, finding a macrocanonical model amounts to minimizing a convex function and sampling from an associated Gibbs measure. We analyze an algorithm which alternates between sampling and minimizing. We present experiments with neural network features and study the drawbacks and advantages of using this sampling scheme.
Entity Linking via Graph-Distance MinimizationRoi Blanco
Entity-linking is a natural-language--processing task that consists in identifying strings of text that refer to a particular
item in some reference knowledge base.
One instance of entity-linking can be formalized as an optimization problem on the underlying concept graph, where the quantity to be optimized is the average distance between chosen items.
Inspired by this application, we define a new graph problem which is a natural variant of the Maximum Capacity Representative Set. We prove that our problem is NP-hard for general graphs; nonetheless, it turns out to be solvable in linear time under some more restrictive assumptions. For the general case, we propose several heuristics: one of these tries to enforce the above assumptions while the others try to optimize similar easier objective functions; we show experimentally how these approaches perform with respect to some baselines on a real-world dataset.
Entity Linking via Graph-Distance MinimizationRoi Blanco
Entity-linking is a natural-language--processing task that consists in identifying strings of text that refer to a particular
item in some reference knowledge base.
One instance of entity-linking can be formalized as an optimization problem on the underlying concept graph, where the quantity to be optimized is the average distance between chosen items.
Inspired by this application, we define a new graph problem which is a natural variant of the Maximum Capacity Representative Set. We prove that our problem is NP-hard for general graphs; nonetheless, it turns out to be solvable in linear time under some more restrictive assumptions. For the general case, we propose several heuristics: one of these tries to enforce the above assumptions while the others try to optimize similar easier objective functions; we show experimentally how these approaches perform with respect to some baselines on a real-world dataset.
Statistical Inference Part II: Types of Sampling DistributionDexlab Analytics
This is an in-depth analysis of the way different types of sampling distribution works focusing on their specific functions and interrelations as part of the discussion on the theory of sampling.
Joint3DShapeMatching - a fast approach to 3D model matching using MatchALS 3...Mamoon Ismail Khalid
we extend the global optimization-based
approach of jointly matching a set of images to jointly
matching a set of 3D meshes. The estimated correspon
dences simultaneously maximize pairwise feature affini
ties and cycle consistency across multiple models. We
show that the low-rank matrix recovery problem can be
efficiently applied to the 3D meshes as well. The fast
alternating minimization algorithm helps to handle real
world practical problems with thousands of features. Ex
perimental results show that, unlike the state-of-the-art
algorithm which rely on semi-definite programming, our
algorithm provides an order of magnitude speed-up along
with competitive performance. Along with the joint shape
matching we propose an approach to apply a distortion
term in pairwise matching, which helps in successfully
matching the reflexive sub-parts of two models distinc
tively. In the end, we demonstrate the applicability of
the algorithm to match a set of 3D meshes of the SCAPE
benchmark database
We will describe and analyze accurate and efficient numerical algorithms to interpolate and approximate the integral of multivariate functions. The algorithms can be applied when we are given the function values at an arbitrary positioned, and usually small, existing sparse set of function values (samples), and additional samples are impossible, or difficult (e.g. expensive) to obtain. The methods are based on local, and global, tensor-product sparse quasi-interpolation methods that are exact for a class of sparse multivariate orthogonal polynomials.
Expectation maximization (EM) algorithm is a popular and powerful mathematical method for parameter estimation in case that there exist both observed data and hidden data. Therefore, EM is appropriate to applications which aim to exploit latent aspects under heterogeneous data. This report focuses on probabilistic finite mixture model which is a popular and successful application of EM, which is fully explained in my book (Nguyen, Tutorial on EM algorithm, 2020, pp. 78-88). I also proposed a special regression model associated with mixture model in which missing values are acceptable.
최근 이수가 되고 있는 Bayesian Deep Learning 관련 이론과 최근 어플리케이션들을 소개합니다. Bayesian Inference 의 이론에 관해서 간단히 설명하고 Yarin Gal 의 Monte Carlo Dropout 의 이론과 어플리케이션들을 소개합니다.
Slides taught in the course of pattern recognition of Professor Zohreh Azimifar at Shiraz University.
The slides are owned by the University of Texas.
اسلاید های تدریس شده درس شناسایی آماری الگو استاد زهره عظیمی فر در دانشگاه شیراز.
اسلاید ها متعلق به دانشگاه تگزاس است.
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Parametric Density Estimation using Gaussian Mixture Models
1. Parametric Density Estimation using Gaussian Mixture Models
Parametric Density Estimation using
Gaussian Mixture Models
An Application of the EM Algorithm
Based on tutorials by Je A. Bilmes and by Ludwig Schwardt
Amirkabir University of Technology Bahman 12, 1389
2. Parametric Density Estimation using Gaussian Mixture Models
Outline
1 Introduction
2 Density Estimation
3 Gaussian Mixture Model
4 Some Results
5 Other Applications of EM
3. Parametric Density Estimation using Gaussian Mixture Models
Outline
1 Introduction
2 Density Estimation
3 Gaussian Mixture Model
4 Some Results
5 Other Applications of EM
4. Parametric Density Estimation using Gaussian Mixture Models
Outline
1 Introduction
2 Density Estimation
3 Gaussian Mixture Model
4 Some Results
5 Other Applications of EM
5. Parametric Density Estimation using Gaussian Mixture Models
Outline
1 Introduction
2 Density Estimation
3 Gaussian Mixture Model
4 Some Results
5 Other Applications of EM
6. Parametric Density Estimation using Gaussian Mixture Models
Outline
1 Introduction
2 Density Estimation
3 Gaussian Mixture Model
4 Some Results
5 Other Applications of EM
7. Parametric Density Estimation using Gaussian Mixture Models
Introduction
Outline
1 Introduction
2 Density Estimation
3 Gaussian Mixture Model
4 Some Results
5 Other Applications of EM
8. Parametric Density Estimation using Gaussian Mixture Models
Introduction
What does EM do?
iterative maximization of the likelihood function
when data has missing values or
when maximization of the likelihood is dicult
data augmentation
X is incomplete data
Z = (X; Y) is the complete data set
9. Parametric Density Estimation using Gaussian Mixture Models
Introduction
What does EM do?
iterative maximization of the likelihood function
when data has missing values or
when maximization of the likelihood is dicult
data augmentation
X is incomplete data
Z = (X; Y) is the complete data set
10. Parametric Density Estimation using Gaussian Mixture Models
Introduction
What does EM do?
iterative maximization of the likelihood function
when data has missing values or
when maximization of the likelihood is dicult
data augmentation
X is incomplete data
Z = (X; Y) is the complete data set
11. Parametric Density Estimation using Gaussian Mixture Models
Introduction
What does EM do?
iterative maximization of the likelihood function
when data has missing values or
when maximization of the likelihood is dicult
data augmentation
X is incomplete data
Z = (X; Y) is the complete data set
12. Parametric Density Estimation using Gaussian Mixture Models
Introduction
What does EM do?
iterative maximization of the likelihood function
when data has missing values or
when maximization of the likelihood is dicult
data augmentation
X is incomplete data
Z = (X; Y) is the complete data set
13. Parametric Density Estimation using Gaussian Mixture Models
Introduction
What does EM do?
iterative maximization of the likelihood function
when data has missing values or
when maximization of the likelihood is dicult
data augmentation
X is incomplete data
Z = (X; Y) is the complete data set
14. Parametric Density Estimation using Gaussian Mixture Models
Introduction
What does EM do?
iterative maximization of the likelihood function
when data has missing values or
when maximization of the likelihood is dicult
data augmentation
X is incomplete data
Z = (X; Y) is the complete data set
15. Parametric Density Estimation using Gaussian Mixture Models
Introduction
EM and MLE
maximum likelihood estimation
estimates density parameters
=) EM app: parametric density estimation
when density is a mixture of Gaussians
16. Parametric Density Estimation using Gaussian Mixture Models
Introduction
EM and MLE
maximum likelihood estimation
estimates density parameters
=) EM app: parametric density estimation
when density is a mixture of Gaussians
17. Parametric Density Estimation using Gaussian Mixture Models
Introduction
EM and MLE
maximum likelihood estimation
estimates density parameters
=) EM app: parametric density estimation
when density is a mixture of Gaussians
18. Parametric Density Estimation using Gaussian Mixture Models
Introduction
EM and MLE
maximum likelihood estimation
estimates density parameters
=) EM app: parametric density estimation
when density is a mixture of Gaussians
19. Parametric Density Estimation using Gaussian Mixture Models
Introduction
EM and MLE
maximum likelihood estimation
estimates density parameters
=) EM app: parametric density estimation
when density is a mixture of Gaussians
20. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
Outline
1 Introduction
2 Density Estimation
3 Gaussian Mixture Model
4 Some Results
5 Other Applications of EM
21. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The density estimation problem
Denition
Our parametric density estimation problem:
X=f xi Ng
i=1 , xi P R D
f (xi jΘ), i.i.d. assumption
€
f (xi jΘ) = K=1 k p(xi jk )
k
K components (given)
Θ = (1 ; : : : ; K ; 1 ; : : : ; K )
22. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The density estimation problem
Denition
Our parametric density estimation problem:
X=f xi Ng
i=1 , xi P R D
f (xi jΘ), i.i.d. assumption
€
f (xi jΘ) = K=1 k p(xi jk )
k
K components (given)
Θ = (1 ; : : : ; K ; 1 ; : : : ; K )
23. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The density estimation problem
Denition
Our parametric density estimation problem:
X=f xi Ng
i=1 , xi P R D
f (xi jΘ), i.i.d. assumption
€
f (xi jΘ) = K=1 k p(xi jk )
k
K components (given)
Θ = (1 ; : : : ; K ; 1 ; : : : ; K )
24. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The density estimation problem
Denition
Our parametric density estimation problem:
X=f xi Ng
i=1 , xi P R D
f (xi jΘ), i.i.d. assumption
€
f (xi jΘ) = K=1 k p(xi jk )
k
K components (given)
Θ = (1 ; : : : ; K ; 1 ; : : : ; K )
25. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The density estimation problem
Denition
Our parametric density estimation problem:
X=f xi Ng
i=1 , xi P R D
f (xi jΘ), i.i.d. assumption
€
f (xi jΘ) = K=1 k p(xi jk )
k
K components (given)
Θ = (1 ; : : : ; K ; 1 ; : : : ; K )
26. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The density estimation problem
Denition
Our parametric density estimation problem:
X=f xi Ng
i=1 , xi P R D
f (xi jΘ), i.i.d. assumption
€
f (xi jΘ) = K=1 k p(xi jk )
k
K components (given)
Θ = (1 ; : : : ; K ; 1 ; : : : ; K )
27. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
Constraint on mixing probabilities i
Sum to unity
p(xi jk )dxi = 1
RD
1= f (xi jΘ)dxi
RD
K
ˆ
= k p(xi jk )dxi
RD k=1
K
ˆ
= k :
k=1
28. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
Constraint on mixing probabilities i
Sum to unity
p(xi jk )dxi = 1
RD
1= f (xi jΘ)dxi
RD
K
ˆ
= k p(xi jk )dxi
RD k=1
K
ˆ
= k :
k=1
29. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The maximum likelihood problem
Denition
Our MLE problem:
N
p(XjΘ) = i=1 p(xi jΘ) = v(ΘjX)
Θ£ = arg maxΘ v(ΘjX)
the log-likelihood: log(v(ΘjX))
€N €K ¡
log(v(ΘjX)) = i=1 log k=1 k p(xi ; k )
dicult to optimize b/c it contains log of sum
30. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The maximum likelihood problem
Denition
Our MLE problem:
N
p(XjΘ) = i=1 p(xi jΘ) = v(ΘjX)
Θ£ = arg maxΘ v(ΘjX)
the log-likelihood: log(v(ΘjX))
€N €K ¡
log(v(ΘjX)) = i=1 log k=1 k p(xi ; k )
dicult to optimize b/c it contains log of sum
31. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The maximum likelihood problem
Denition
Our MLE problem:
N
p(XjΘ) = i=1 p(xi jΘ) = v(ΘjX)
Θ£ = arg maxΘ v(ΘjX)
the log-likelihood: log(v(ΘjX))
€N €K ¡
log(v(ΘjX)) = i=1 log k=1 k p(xi ; k )
dicult to optimize b/c it contains log of sum
32. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The maximum likelihood problem
Denition
Our MLE problem:
N
p(XjΘ) = i=1 p(xi jΘ) = v(ΘjX)
Θ£ = arg maxΘ v(ΘjX)
the log-likelihood: log(v(ΘjX))
€N €K ¡
log(v(ΘjX)) = i=1 log k=1 k p(xi ; k )
dicult to optimize b/c it contains log of sum
33. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The maximum likelihood problem
Denition
Our MLE problem:
N
p(XjΘ) = i=1 p(xi jΘ) = v(ΘjX)
Θ£ = arg maxΘ v(ΘjX)
the log-likelihood: log(v(ΘjX))
€N €K ¡
log(v(ΘjX)) = i=1 log k=1 k p(xi ; k )
dicult to optimize b/c it contains log of sum
34. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The maximum likelihood problem
Denition
Our MLE problem:
N
p(XjΘ) = i=1 p(xi jΘ) = v(ΘjX)
Θ£ = arg maxΘ v(ΘjX)
the log-likelihood: log(v(ΘjX))
€N €K ¡
log(v(ΘjX)) = i=1 log k=1 k p(xi ; k )
dicult to optimize b/c it contains log of sum
35. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The EM formulation
Denition
Our EM problem:
Z = (X; Y)
v(ΘjZ) = v(ΘjX; Y) = p(X; YjΘ)
yi P 1 : : : K
yi = k if the ith sample was generated by the kth component
Y is a random vector
€N
log(v(ΘjX; Y)) = i=1 log( yi p(xi jyi ))
36. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The EM formulation
Denition
Our EM problem:
Z = (X; Y)
v(ΘjZ) = v(ΘjX; Y) = p(X; YjΘ)
yi P 1 : : : K
yi = k if the ith sample was generated by the kth component
Y is a random vector
€N
log(v(ΘjX; Y)) = i=1 log( yi p(xi jyi ))
37. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The EM formulation
Denition
Our EM problem:
Z = (X; Y)
v(ΘjZ) = v(ΘjX; Y) = p(X; YjΘ)
yi P 1 : : : K
yi = k if the ith sample was generated by the kth component
Y is a random vector
€N
log(v(ΘjX; Y)) = i=1 log( yi p(xi jyi ))
38. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The EM formulation
Denition
Our EM problem:
Z = (X; Y)
v(ΘjZ) = v(ΘjX; Y) = p(X; YjΘ)
yi P 1 : : : K
yi = k if the ith sample was generated by the kth component
Y is a random vector
€N
log(v(ΘjX; Y)) = i=1 log( yi p(xi jyi ))
39. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The EM formulation
Denition
Our EM problem:
Z = (X; Y)
v(ΘjZ) = v(ΘjX; Y) = p(X; YjΘ)
yi P 1 : : : K
yi = k if the ith sample was generated by the kth component
Y is a random vector
€N
log(v(ΘjX; Y)) = i=1 log( yi p(xi jyi ))
40. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The EM formulation
Denition
Our EM problem:
Z = (X; Y)
v(ΘjZ) = v(ΘjX; Y) = p(X; YjΘ)
yi P 1 : : : K
yi = k if the ith sample was generated by the kth component
Y is a random vector
€N
log(v(ΘjX; Y)) = i=1 log( yi p(xi jyi ))
41. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The EM formulation
Denition
Our EM problem:
Z = (X; Y)
v(ΘjZ) = v(ΘjX; Y) = p(X; YjΘ)
yi P 1 : : : K
yi = k if the ith sample was generated by the kth component
Y is a random vector
€N
log(v(ΘjX; Y)) = i=1 log( yi p(xi jyi ))
42. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The EM formulation
Continued
Denition
The E- and M-steps with the auxiliary function Q:
E-step: Q(Θ; Θ(i 1) ) = E [log(p(X; YjΘ))jX; Θ(i 1) ]
M-step: Θ(i) = arg maxΘ Q(Θ; Θ(i 1) )
43. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The EM formulation
Continued
Denition
The E- and M-steps with the auxiliary function Q:
E-step: Q(Θ; Θ(i 1) ) = E [log(p(X; YjΘ))jX; Θ(i 1) ]
M-step: Θ(i) = arg maxΘ Q(Θ; Θ(i 1) )
44. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The EM formulation
Continued
Denition
The E- and M-steps with the auxiliary function Q:
E-step: Q(Θ; Θ(i 1) ) = E [log(p(X; YjΘ))jX; Θ(i 1) ]
M-step: Θ(i) = arg maxΘ Q(Θ; Θ(i 1) )
45. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The EM formulation
The Q function
Q(Θ; Θ(t) ) = E [log(p(X; YjΘ))jX; Θ(t) ]
K N
ˆˆ
= log(k p(xi jk ))p(kjxi ; Θ(t) )
k=1 i=1
K N
ˆˆ
= log(k )p(kjxi ; Θ(t) )
k=1 i=1
K N
ˆˆ
+ log(p(xi jk ))p(kjxi ; Θ(t) )
k=1 i=1
46. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The EM formulation
Solving for k
€
We use the Lagrange multiplier to enforce k k = 1.
@ ˆˆ ˆ
[ log(k )p(kjxi ; Θ(t) ) + ( k 1)] = 0
@k k i k
ˆ 1
p(kjxi ; Θ(t) ) + = 0
i
k
Summing both sides over k, we get = N .
1 ˆ
k = p(kjxi ; Θ(t) ):
N i
47. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The EM formulation
Solving for k
€
We use the Lagrange multiplier to enforce k k = 1.
@ ˆˆ ˆ
[ log(k )p(kjxi ; Θ(t) ) + ( k 1)] = 0
@k k i k
ˆ 1
p(kjxi ; Θ(t) ) + = 0
i
k
Summing both sides over k, we get = N .
1 ˆ
k = p(kjxi ; Θ(t) ):
N i
48. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The EM formulation
Solving for k
€
We use the Lagrange multiplier to enforce k k = 1.
@ ˆˆ ˆ
[ log(k )p(kjxi ; Θ(t) ) + ( k 1)] = 0
@k k i k
ˆ 1
p(kjxi ; Θ(t) ) + = 0
i
k
Summing both sides over k, we get = N .
1 ˆ
k = p(kjxi ; Θ(t) ):
N i
49. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The EM formulation
Solving for k
€
We use the Lagrange multiplier to enforce k k = 1.
@ ˆˆ ˆ
[ log(k )p(kjxi ; Θ(t) ) + ( k 1)] = 0
@k k i k
ˆ 1
p(kjxi ; Θ(t) ) + = 0
i
k
Summing both sides over k, we get = N .
1 ˆ
k = p(kjxi ; Θ(t) ):
N i
50. Parametric Density Estimation using Gaussian Mixture Models
Density Estimation
The EM formulation
Solving for k
€
We use the Lagrange multiplier to enforce k k = 1.
@ ˆˆ ˆ
[ log(k )p(kjxi ; Θ(t) ) + ( k 1)] = 0
@k k i k
ˆ 1
p(kjxi ; Θ(t) ) + = 0
i
k
Summing both sides over k, we get = N .
1 ˆ
k = p(kjxi ; Θ(t) ):
N i
51. Parametric Density Estimation using Gaussian Mixture Models
Gaussian Mixture Model
Outline
1 Introduction
2 Density Estimation
3 Gaussian Mixture Model
4 Some Results
5 Other Applications of EM
52. Parametric Density Estimation using Gaussian Mixture Models
Gaussian Mixture Model
GMM
Solving for mk and Σk
Gaussian components
D
p(xi jxk ; Σk ) = (2) 2 jΣk j 2 exp( 1 (xi mk )T Σ 1 (xi mk ))
1
2 k
€
i xi p(kjxi ; Θ(t) )
mk = €
i p(kjxi ; Θ )
(t)
p(kjxi ; Θ(t) )(xi mk )(xi mk )T
Σk =
p(kjxi ; Θ(t) )
53. Parametric Density Estimation using Gaussian Mixture Models
Gaussian Mixture Model
GMM
Solving for mk and Σk
Gaussian components
D
p(xi jxk ; Σk ) = (2) 2 jΣk j 2 exp( 1 (xi mk )T Σ 1 (xi mk ))
1
2 k
€
i xi p(kjxi ; Θ(t) )
mk = €
i p(kjxi ; Θ )
(t)
p(kjxi ; Θ(t) )(xi mk )(xi mk )T
Σk =
p(kjxi ; Θ(t) )
54. Parametric Density Estimation using Gaussian Mixture Models
Gaussian Mixture Model
Choice of Σk
Full covariance
Fits data best, but costly in high-dimensional space.
Figure: full covariance
55. Parametric Density Estimation using Gaussian Mixture Models
Gaussian Mixture Model
Choice of Σk
Diagonal covariance
A tradeo between cost and quality.
Figure: diagonal covariance
56. Parametric Density Estimation using Gaussian Mixture Models
Gaussian Mixture Model
Choice of Σk
Spherical covariance, Σk = k I
2
Needs many components to cover data.
Figure: spherical covariance
Decision should be based on the size of training data set
so that all parameters may be tuned
57. Parametric Density Estimation using Gaussian Mixture Models
Gaussian Mixture Model
Choice of Σk
Spherical covariance, Σk = k I
2
Needs many components to cover data.
Figure: spherical covariance
Decision should be based on the size of training data set
so that all parameters may be tuned
58. Parametric Density Estimation using Gaussian Mixture Models
Gaussian Mixture Model
Choice of Σk
Spherical covariance, Σk = k I
2
Needs many components to cover data.
Figure: spherical covariance
Decision should be based on the size of training data set
so that all parameters may be tuned
59. Parametric Density Estimation using Gaussian Mixture Models
Some Results
Outline
1 Introduction
2 Density Estimation
3 Gaussian Mixture Model
4 Some Results
5 Other Applications of EM
60. Parametric Density Estimation using Gaussian Mixture Models
Some Results
Data
data generated from a mixture of three bivariate Gaussians
61. Parametric Density Estimation using Gaussian Mixture Models
Some Results
PDFs
estimated pdf contours (after 43 iterations)
62. Parametric Density Estimation using Gaussian Mixture Models
Some Results
Clusters
xi assigned to the cluster k corresponding to the highest p(kjxi ; Θ)
63. Parametric Density Estimation using Gaussian Mixture Models
Other Applications of EM
Outline
1 Introduction
2 Density Estimation
3 Gaussian Mixture Model
4 Some Results
5 Other Applications of EM
64. Parametric Density Estimation using Gaussian Mixture Models
Other Applications of EM
A problem with missing data
Life-testing experiment
lifetime of light bulbs follows an exponential distribution with
mean
two separate experiments
N bulbs tested until failed
failure times recorded as x1 ; : : : ; x N
M bulbs tested
failure times not recorded
only number of bulbs , that failed at time
r t
missing data are failure times 1 M u ;:::;u
65. Parametric Density Estimation using Gaussian Mixture Models
Other Applications of EM
A problem with missing data
Life-testing experiment
lifetime of light bulbs follows an exponential distribution with
mean
two separate experiments
N bulbs tested until failed
failure times recorded as x1 ; : : : ; x N
M bulbs tested
failure times not recorded
only number of bulbs , that failed at time
r t
missing data are failure times 1 M u ;:::;u
66. Parametric Density Estimation using Gaussian Mixture Models
Other Applications of EM
A problem with missing data
Life-testing experiment
lifetime of light bulbs follows an exponential distribution with
mean
two separate experiments
N bulbs tested until failed
failure times recorded as x1 ; : : : ; x N
M bulbs tested
failure times not recorded
only number of bulbs , that failed at time
r t
missing data are failure times 1 M u ;:::;u
67. Parametric Density Estimation using Gaussian Mixture Models
Other Applications of EM
A problem with missing data
Life-testing experiment
lifetime of light bulbs follows an exponential distribution with
mean
two separate experiments
N bulbs tested until failed
failure times recorded as x1 ; : : : ; x N
M bulbs tested
failure times not recorded
only number of bulbs , that failed at time
r t
missing data are failure times 1 M u ;:::;u
68. Parametric Density Estimation using Gaussian Mixture Models
Other Applications of EM
A problem with missing data
Life-testing experiment
lifetime of light bulbs follows an exponential distribution with
mean
two separate experiments
N bulbs tested until failed
failure times recorded as x1 ; : : : ; x N
M bulbs tested
failure times not recorded
only number of bulbs , that failed at time
r t
missing data are failure times 1 M u ;:::;u
69. Parametric Density Estimation using Gaussian Mixture Models
Other Applications of EM
A problem with missing data
Life-testing experiment
lifetime of light bulbs follows an exponential distribution with
mean
two separate experiments
N bulbs tested until failed
failure times recorded as x1 ; : : : ; x N
M bulbs tested
failure times not recorded
only number of bulbs , that failed at time
r t
missing data are failure times 1 M u ;:::;u
70. Parametric Density Estimation using Gaussian Mixture Models
Other Applications of EM
A problem with missing data
Life-testing experiment
lifetime of light bulbs follows an exponential distribution with
mean
two separate experiments
N bulbs tested until failed
failure times recorded as x1 ; : : : ; x N
M bulbs tested
failure times not recorded
only number of bulbs , that failed at time
r t
missing data are failure times 1 M u ;:::;u
71. Parametric Density Estimation using Gaussian Mixture Models
Other Applications of EM
A problem with missing data
Life-testing experiment
lifetime of light bulbs follows an exponential distribution with
mean
two separate experiments
N bulbs tested until failed
failure times recorded as x1 ; : : : ; x N
M bulbs tested
failure times not recorded
only number of bulbs , that failed at time
r t
missing data are failure times 1 M u ;:::;u
72. Parametric Density Estimation using Gaussian Mixture Models
Other Applications of EM
A problem with missing data
Life-testing experiment
lifetime of light bulbs follows an exponential distribution with
mean
two separate experiments
N bulbs tested until failed
failure times recorded as x1 ; : : : ; x N
M bulbs tested
failure times not recorded
only number of bulbs , that failed at time
r t
missing data are failure times 1 M u ;:::;u
73. Parametric Density Estimation using Gaussian Mixture Models
Other Applications of EM
EM formulation
Life-testing experiment
€M
log(v(jx; u)) = N (log + x=)
i (log + ui =)
expected value for bulb still burning: t +
t k
one that burned out: 1tee = (k) = th(k)
( )
t =
E-step:
Q(; (k) ) = (N + M ) log
1 (N x + (M r)(t + k ) + r( k th k ))
( ) ( ) ( )
M-step: 1
N +M ( N x + (M r)(t + (k) ) + r((k) th(k) ))
74. Parametric Density Estimation using Gaussian Mixture Models
Other Applications of EM
EM formulation
Life-testing experiment
€M
log(v(jx; u)) = N (log + x=)
i (log + ui =)
expected value for bulb still burning: t +
t k
one that burned out: 1tee = (k) = th(k)
( )
t =
E-step:
Q(; (k) ) = (N + M ) log
1 (N x + (M r)(t + k ) + r( k th k ))
( ) ( ) ( )
M-step: 1
N +M ( N x + (M r)(t + (k) ) + r((k) th(k) ))
75. Parametric Density Estimation using Gaussian Mixture Models
Other Applications of EM
EM formulation
Life-testing experiment
€M
log(v(jx; u)) = N (log + x=)
i (log + ui =)
expected value for bulb still burning: t +
t k
one that burned out: 1tee = (k) = th(k)
( )
t =
E-step:
Q(; (k) ) = (N + M ) log
1 (N x + (M r)(t + k ) + r( k th k ))
( ) ( ) ( )
M-step: 1
N +M ( N x + (M r)(t + (k) ) + r((k) th(k) ))
76. Parametric Density Estimation using Gaussian Mixture Models
Other Applications of EM
EM formulation
Life-testing experiment
€M
log(v(jx; u)) = N (log + x=)
i (log + ui =)
expected value for bulb still burning: t +
t k
one that burned out: 1tee = (k) = th(k)
( )
t =
E-step:
Q(; (k) ) = (N + M ) log
1 (N x + (M r)(t + k ) + r( k th k ))
( ) ( ) ( )
M-step: 1
N +M ( N x + (M r)(t + (k) ) + r((k) th(k) ))
77. Parametric Density Estimation using Gaussian Mixture Models
Other Applications of EM
EM formulation
Life-testing experiment
€M
log(v(jx; u)) = N (log + x=)
i (log + ui =)
expected value for bulb still burning: t +
t k
one that burned out: 1tee = (k) = th(k)
( )
t =
E-step:
Q(; (k) ) = (N + M ) log
1 (N x + (M r)(t + k ) + r( k th k ))
( ) ( ) ( )
M-step: 1
N +M ( N x + (M r)(t + (k) ) + r((k) th(k) ))
78. Parametric Density Estimation using Gaussian Mixture Models
Other Applications of EM
EM formulation
Life-testing experiment
€M
log(v(jx; u)) = N (log + x=)
i (log + ui =)
expected value for bulb still burning: t +
t k
one that burned out: 1tee = (k) = th(k)
( )
t =
E-step:
Q(; (k) ) = (N + M ) log
1 (N x + (M r)(t + k ) + r( k th k ))
( ) ( ) ( )
M-step: 1
N +M ( N x + (M r)(t + (k) ) + r((k) th(k) ))