Clustering：k-means, expect-maximization and gaussian mixture model

XJinliaXXngXXXXXXXX
jlxu@bupt.edu.cn
Department of Computer Science
Institute of Network Technology . BUPT
May 20, 2016
K-means, E.M. and Miture ModelsK-means, E.M. and Mixture Models

Remind:Two``-MainProblemsinML
• Two-mainproblemsinML:
– Regression: Linear Regression, Neural net...
– Classification: Decision Tree, kNN, Bayessian Classifier...
• Today, we will learn:
– K-means: a trivial unsupervised classification algorithm.
– Expectation Maximization: a general algorithm for density estimation.
∗ We will see how to use EM in general cases and in specific case of GMM.
– GMM: a tool for modelling Data-in-the-Wild (density estimator)
∗ We also learn how to use GMM in a Bayessian Classifier

Contents
• Unsupervised Learning
• K-means clustering
• Expectation Maximization (E.M.)
• Gaussian mixtures as a Density Estimator
– Gaussian mixtures
– EM for mixtures
• Gaussian mixtures for classiﬁcation

Unsupervised Learning
•
– Label of each sample is included in the training set
Sample Label
x1 y1
... ...
xn yk
• Unsupervised learning:
– Traning set contains the samples only
Sample Label
x1
...
xn
Supervised learning techniques:

Unsupervised Learning
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(a) Supervised learning.
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(b) Unsupervised learning.
Figure 1: Unsupervised vs. Supervised Learning

What is unsupervised learning useful for?
• Collecting and labeling a large training set can be very expensive.
• Be able to ﬁnd features which are helpful for categorization.
• Gain insight into the natural structure of the data.

K-means clustering
• Clustering algorithms aim to ﬁnd
groups of “similar” data points among
the input data.
• K-means is an eﬀective algorithm to ex-
tract a given number of clusters from a
training set.
• Once done, the cluster locations can
be used to classify data into distinct
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K-means clustering
• Given:
– The dataset: {xn}N
n=1 = {x1, x2, ..., xN}
– Number of clusters: K (K < N)
• Goal: ﬁnd a partition S = {Sk}K
k=1 so that it minimizes the objective function
J =
N∑
n=1
K∑
k=1
rnk ∥ xn − µk ∥2
(1)
where rnk = 1 if xn is assigned to cluster Sk, and rnj = 0 for j ̸= k.
i.e. Find values for the {rnk} and the {µk} to minimize (1).

K-means clustering
J =
N∑
n=1
K∑
k=1
• Select some initial values for the µk.
• Expectation: keep the µk ﬁxed, minimize J respect to rnk.
• Maximization: keep the rnk ﬁxed, minimize J respect to the µk.
• Loop until no change in the partitions (or maximum number of interations is
exceeded).

K-means clustering
J =
N∑
n=1
K∑
k=1
• Expectation: J is linear function of rnk
rnk =



1 if k = arg minj ∥ xn − µj ∥2
0 otherwise
• Maximization: setting the derivative of J with respect to µk to zero, gives:
µk =
∑
n rnkxn
∑
n rnk
Convergence of K-means: assured [why?], but may lead to local minimum of J
[8]

K-means clustering: How to understand?
J =
N∑
n=1
K∑
k=1
• Expectation: minimize J respect to rnk
– For each xn, ﬁnd the “closest” cluster mean µk and put xn into cluster Sk.
• Maximization: minimize J respect to µk
– For each cluster Sk, re-estimate the cluster mean µk to be the average value
of all samples in Sk.
• Loop until no change in the partitions (or maximum number of interations is
exceeded).

Initialize with random clusters

Assign each point to nearest center

Recompute optimum centers (means)

Repeat: Assign points to nearest center

Repeat...Until clustering does not change

Repeat...Until clustering does not change
Total error reduced at every step - guaranteed to converge.

K-means clustering: some variations
• Initial cluster centroids:
– Randomly selected
– Iterative procedure: k-mean++ [2]
• Number of clusters K:
– Empirically/experimentally: 2 ∼
√
n
– Learning [6]
• Objective function:
– General dissimilarity measure: k-medoids algorithm.
• Speeding up:
– kd-trees for pre-processing [7]
– Triangle inequality for distance calculation [4]

Expectation Maximization
• A general-purpose algorithm for MLE in a wide range of situations.
• First formally stated by Dempster, Laird and Rubin in 1977 [1]
• An excellent way of doing our unsupervised learning problem, as we will see
– EM is also used widely in other domains.

EM: a solution for MLE
• Given a statistical model with:
– a set X of observed data,
– a set Z of unobserved latent data,
– a vector of unknown parameters θ,
– a likelihood function L (θ; X, Z) = p (X, Z | θ)
• Roughly speaking, the aim of MLE is to determine θ = arg maxθ L (θ; X, Z)
– We known the old trick: partial derivatives of the log likelihood...
– But it is not always tractable [e.g.]
– Other solutions are available.

EM: General Case
L (θ; X, Z) = p (X, Z | θ)
• EM is just an iterative procedure for ﬁnding the MLE
• Expectation step: keep the current estimate θ(t)
ﬁxed, calculate the expected
value of the log likelihood function
Q
(
θ | θ(t)
)
= E [log L (θ; X, Z)] = E [log p (X, Z | θ)]
• Maximization step: Find the parameter that maximizes this quantity
θ(t+1)
= arg max
θ
Q
(
θ | θ(t)
)

EM: Motivation
• If we know the value of the parameters θ, we can ﬁnd the value of latent variables
Z by maximizing the log likelihood over all possible values of Z
– Searching on the value space of Z.
• If we know Z, we can ﬁnd an estimate of θ
– Typically by grouping the observed data points according to the value of asso-
ciated latent variable,
– then averaging the values (or some functions of the values) of the points in
each group.
To understand this motivation, let’s take K-means as a trivial example...

EM: informal description
Both θ and Z are unknown, EM is an iterative algorithm:
1. Initialize the parameters θ to some random values.
2. Compute the best values of Z given these parameter values.
3. Use the just-computed values of Z to ﬁnd better estimates for θ.
4. Iterate until convergence.

EM Convergence
• E.M. Convergence: Yes
– After each iteration, p (X, Z | θ) must increase or remain [NOT OBVIOUS]
– But it can not exceed 1 [OBVIOUS]
– Hence it must converge [OBVIOUS]
• Bad news: E.M. converges to local optimum.
– Whether the algorithm converges to the global optimum depends on the ini-
tialization.
• Let’s take K-means as an example, again...
• Details can be found in [9].

Contents
– EM for mixtures
–

Remind: Bayes Classiﬁer
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p (y = i | x) =
p (x | y = i) p (y = i)
p (x)

Remind: Bayes Classiﬁer
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In case of Gaussian Bayes Classiﬁer:
p (y = i | x) =
1
(2π)
d/2
∥Σi∥1/2
exp
[
−1
2 (x − µi)T
Σi (x − µi)
]
pi
p (x)
How can we deal with the denominator p (x)?

Remind: The Single Gaussian Distribution
• Multivariate Gaussian
N (x; µ, Σ) =
1
(2π)
d/2
∥ Σ ∥1/2
exp


−
1
2
(x − µ)T
Σ−1
(x − µ)



• For maximum likelihood
0 =
∂ ln N (x1, x2, ..., xN; µ, Σ)
∂µ
• and the solution is
µML =
1
N
N∑
i=1
xi
ΣML =
1
N
N∑
i=1
(xi − µML)T
(xi − µML)

The GMM assumption
• There are k components: {ci}k
i=1
• Component ci has an associated mean
vector µi
•
•
µ1
µ2
µ3

The GMM assumption
i=1
vector µi
• Each component generates data from a
Gaussian with mean µi and covariance
matrix Σi
• Each sample is generated according to
the following guidelines:
µ1
µ2
µ3

The GMM assumption
i=1
vector µi
matrix Σi
– Randomly select component ci
with probability P (ci) = wi, s.t.
∑k
i=1 wi = 1
µ2

The GMM assumption
i=1
vector µi
matrix Σi
– Randomly select component ci with
probability P (ci) = wi, s.t.
∑k
i=1 wi = 1
– Sample ~ N (µi, Σi)
µ2
x

Probability density function of GMM
“Linear combination” of Gaussians:
f (x) =
k∑
i=1
wiN (x; µi, Σi) , where
k∑
i=1
wi = 1
0 50 100 150 200 250
0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
w1N µ1, σ2
1 w2N µ2, σ2
2
w3N µ3, σ2
3
f (x)
(a) The pdf of an 1D GMM with 3 components. (b) The pdf of an 2D GMM with 3 components.
Figure 2: Probability density function of some GMMs.

GMM: Problem deﬁnition
f (x) =
k∑
i=1
k∑
i=1
wi = 1
Given a training set, how to model these data point using GMM?
• Given:
– The trainning set: {xi}N
i=1
– Number of clusters: k
• Goal: model this data using a mixture of Gaussians
– Weights: w1, w2, ..., wk
– Means and covariances: µ1, µ2, ..., µk; Σ1, Σ2, ..., Σk

Computing likelihoods in unsupervised case
f (x) =
k∑
i=1
k∑
i=1
wi = 1
• Given a mixture of Gaussians, denoted by G. For any x, we can deﬁne the
likelihood:
P (x | G) = P (x | w1, µ1, Σ1, ..., wk, µk, Σk)
=
k∑
i=1
P (x | ci) P (ci)
=
k∑
i=1
wiN (x; µi, Σi)
• So we can deﬁne likelihood for the whole training set [Why?]
P (x1, x2, ..., xN | G) =
N∏
i=1
P (xi | G)
=
N∏
i=1
k∑
j=1
wjN (xi; µj, Σj)

Estimating GMM parameters
• We known this: Maximum Likelihood Estimation
ln P (X | G) =
N∑
i=1
ln



k∑
j=1
wjN (xi; µj, Σj)



– For the max likelihood:
0 =
∂ ln P (X | G)
∂µj
– This leads to non-linear non-analytically-solvable equations!
• Use gradient descent
– Slow but doable
• A much cuter and recently popular method...

E.M. for GMM
• Remember:
– We have the training set {xi}N
i=1, the number of components k.
– Assume we know p (c1) = w1, p (c2) = w2, ..., p (ck) = wk
– We don’t know µ1, µ2, ..., µk
The likelihood:
p (data | µ1, µ2, ..., µk) = p (x1, x2, ..., xN | µ1, µ2, ..., µk)
=
N∏
i=1
p (xi | µ1, µ2, ..., µk)
=
N∏
i=1
k∑
j=1
p (xi | wj, µ1, µ2, ..., µk) p (cj)
=
N∏
i=1
k∑
j=1
K exp


−
1
2σ2
(
xi − µj
)
2


wi

E.M. for GMM
• For Max. Likelihood, we know ∂
∂µi
log p (data | µ1, µ2, ..., µk) = 0
• Some wild algebra turns this into: For Maximum Likelihood, for each j:
µj =
N∑
i=1
p (cj | xi, µ1, µ2, ..., µk) xi
N∑
i=1
p (cj | xi, µ1, µ2, ..., µk)
This is N non-linear equations of µj’s.
• So:
– If, for each xi, we know p (cj | xi, µ1, µ2, ..., µk), then we could easily compute
µj,
– If we know each µj, we could compute p (cj | xi, µ1, µ2, ..., µk) for each xi
and cj.

E.M. for GMM
• E.M. is coming: on the t’th iteration, let our estimates be
λt = {µ1 (t) , µ2 (t) , ..., µk (t)}
• E-step: compute the expected classes of all data points for each class
p (cj | xi, λt) =
p (xi | cj, λt) p (cj | λt)
p (xi | λt)
=
p
(
xi | cj, µj (t) , σjI
)
p (cj)
k∑
m=1
p (xi | cm, µm (t) , σmI) p (cm)
• M-step: compute µ given our data’s class membership distributions
µj (t + 1) =
N∑
i=1
p (cj | xi, λt) xi
N∑
i=1
p (cj | xi, λt)

E.M. for General GMM: E-step
• On the t’th iteration, let our estimates be
λt = {µ1 (t) , µ2 (t) , ..., µk (t) , Σ1 (t) , Σ2 (t) , ..., Σk (t) , w1 (t) , w2 (t) , ..., wk (t)}
• E-step: compute the expected classes of all data points for each class
τij (t) ≡ p (cj | xi, λt) =
p (xi | cj, λt) p (cj | λt)
p (xi | λt)
=
p
(
xi | cj, µj (t) , Σj (t)
)
wj (t)
k∑
m=1
p (xi | cm, µm (t) , Σj (t)) wm (t)

E.M. for General GMM: M-step
• M-step: compute µ given our data’s class membership distributions
wj (t + 1) =
N∑
i=1
p (cj | xi, λt)
N
µj (t + 1) =
N∑
i=1
p (cj | xi, λt) xi
N∑
i=1
p (cj | xi, λt)
=
1
N
N∑
i=1
τij (t) =
1
Nwj (t + 1)
N∑
i=1
τij (t) xi
Σj (t + 1) =
N∑
i=1
p (cj | xi, λt)
[
xi − µj (t + 1)
] [
xi − µj (t + 1)
]
T
N∑
i=1
p (cj | xi, λt)
=
1
Nwj (t + 1)
N∑
i=1
τij (t)
[
xi − µj (t + 1)
] [
xi − µj (t + 1)
]
T

E.M. for General GMM: Initialization
• wj = 1/k, j = 1, 2, ..., k
• Each µj is set to a randomly selected point
– Or use K-means for this initialization.
• Each Σj is computed using the equation in previous slide...

Gaussian Mixture Example: Start

Local optimum solution
• E.M. is guaranteed to ﬁnd the local optimal solution by monotonically increasing
the log-likelihood
• Whether it converges to the global optimal solution depends on the initialization
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GMM: Selecting the number of components
• We can run the E.M. algorithm with diﬀerent numbers of components.
– Need a criteria for selecting the “best” number of components
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Contents
– Regularized EM
– Model Selection
– EM for mixtures

Gaussian mixtures for classiﬁcation
p (y = i | x) =
p (x | y = i) p (y = i)
p (x)
• To build a Bayesian classiﬁer based on GMM, we can use GMM to model data in
each class
– So each class is modeled by one k-component GMM.
• For example:
Class 0: p (y = 0) , p (x | θ0), (a 3-component mixture)
...

GMM for Classiﬁcation
• As previous, each class is modeled by a k-component GMM.
• A new test sample x is classiﬁed according to
c = arg max
i
p (y = i) p (x | θi)
where
p (x | θi) =
k∑
i=1
wiN (x; µi, Σi)
• Simple, quick (and is actually used!)

Clustering：k-means, expect-maximization and gaussian mixture model

References
[1] N. Laird A. Dempster and D. Rubin. Maximum likelihood from incomplete data
via the em algorithm. Journal of the Royal Statistical Society. Series B (Method-
ological), 39(1):pp. 1–38., 1977.
[2] David Arthur and Sergei Vassilvitskii. k-means ++ : The Advantages of Careful
Seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on
Discrete algorithms, volume 8, pages 1027–1035, 2007.
[3] N. Gumerov C. Yang, R. Duraiswami and L. Davis. Improved fast gauss transform
and eﬃcient kernel density estimation. In IEEE International Conference on
Computer Vision, pages pages 464–471, 2003.
[4] Charles Elkan. Using the Triangle Inequality to Accelerate k-Means. In Proceed-

ings of the Twentieth International Conference on Machine Learning (ICML),
2003.
[5] Keshu Zhang Haifeng Li and Tao Jiang. The regularized em algorithm. In
Proceedings of the 20th National Conference on Artificial Intelligence, pages
pages 807 – 812, Pittsburgh, PA, 2005.
[6] Greg Hamerly and Charles Elkan. Learning the k in k-means. In In Neural
Information Processing Systems. MIT Press, 2003.
[7] Tapas Kanungo, David M Mount, Nathan S Netanyahu, Christine D Piatko, Ruth
Silverman, and Angela Y Wu. An efficient k-means clustering algorithm: anal-
ysis and implementation. IEEE Transactions on Pattern Analysis and Machine
Intelligence, 24(7):881–892, July 2002.
[8] J MacQueen. Some methods for classification and analysis of multivariate obser-
vations. In Proceedings of 5th Berkeley Symposium on Mathematical Statistics
and Probability, volume 233, pages 281–297. University of California Press, 1967.

[9] C.F. Wu. On the convergence properties of the em algorithm. The Annals of
Statistics, 11:95–103, 1983.

Clustering：k-means, expect-maximization and gaussian mixture model

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