Expectation–maximization algorithm
In
History
The EM algorithm was explained and given its name in a classic 1977 paper by Arthur Dempster, Nan Laird, and Donald Rubin.[3] They pointed out that the method had been "proposed many times in special circumstances" by earlier authors. One of the earliest is the gene-counting method for estimating allele frequencies by Cedric Smith.[4] Another was proposed by H.O. Hartley in 1958, and Hartley and Hocking in 1977, from which many of the ideas in the Dempster–Laird–Rubin paper originated.[5] Another one by S.K Ng, Thriyambakam Krishnan and G.J McLachlan in 1977.[6] Hartley’s ideas can be broadened to any grouped discrete distribution. A very detailed treatment of the EM method for exponential families was published by Rolf Sundberg in his thesis and several papers,[7][8][9] following his collaboration with Per Martin-Löf and Anders Martin-Löf.[10][11][12][13][14] The Dempster–Laird–Rubin paper in 1977 generalized the method and sketched a convergence analysis for a wider class of problems. The Dempster–Laird–Rubin paper established the EM method as an important tool of statistical analysis. See also Meng and van Dyk (1997).
The convergence analysis of the Dempster–Laird–Rubin algorithm was flawed and a correct convergence analysis was published by C. F. Jeff Wu in 1983.[15] Wu's proof established the EM method's convergence also outside of the exponential family, as claimed by Dempster–Laird–Rubin.[15]
Introduction
The EM algorithm is used to find (local)
Finding a maximum likelihood solution typically requires taking the derivatives of the likelihood function with respect to all the unknown values, the parameters and the latent variables, and simultaneously solving the resulting equations. In statistical models with latent variables, this is usually impossible. Instead, the result is typically a set of interlocking equations in which the solution to the parameters requires the values of the latent variables and vice versa, but substituting one set of equations into the other produces an unsolvable equation.
The EM algorithm proceeds from the observation that there is a way to solve these two sets of equations numerically. One can simply pick arbitrary values for one of the two sets of unknowns, use them to estimate the second set, then use these new values to find a better estimate of the first set, and then keep alternating between the two until the resulting values both converge to fixed points. It's not obvious that this will work, but it can be proven in this context. Additionally, it can be proven that the derivative of the likelihood is (arbitrarily close to) zero at that point, which in turn means that the point is either a local maximum or a
Description
The symbols
Given the statistical model which generates a set of observed data, a set of unobserved latent data or
However, this quantity is often intractable since is unobserved and the distribution of is unknown before attaining .
The EM algorithm
The EM algorithm seeks to find the maximum likelihood estimate of the marginal likelihood by iteratively applying these two steps:
- Expectation step (E step): Define as the expected value of the log likelihood function of , with respect to the current conditional distribution of given and the current estimates of the parameters :
- Maximization step (M step): Find the parameters that maximize this quantity:
More succinctly, we can write it as one equation:
Interpretation of the variables
The typical models to which EM is applied use as a latent variable indicating membership in one of a set of groups:
- The observed data points may be continuous(taking values in an uncountably infinite set). Associated with each data point may be a vector of observations.
- The latent variables) arediscrete, drawn from a fixed number of values, and with one latent variable per observed unit.
- The parameters are continuous, and are of two kinds: Parameters that are associated with all data points, and those associated with a specific value of a latent variable (i.e., associated with all data points whose corresponding latent variable has that value).
However, it is possible to apply EM to other sorts of models.
The motivation is as follows. If the value of the parameters is known, usually the value of the latent variables can be found by maximizing the log-likelihood over all possible values of , either simply by iterating over or through an algorithm such as the Viterbi algorithm for hidden Markov models. Conversely, if we know the value of the latent variables , we can find an estimate of the parameters fairly easily, typically by simply grouping the observed data points according to the value of the associated latent variable and averaging the values, or some function of the values, of the points in each group. This suggests an iterative algorithm, in the case where both and are unknown:
- First, initialize the parameters to some random values.
- Compute the probability of each possible value of , given .
- Then, use the just-computed values of to compute a better estimate for the parameters .
- Iterate steps 2 and 3 until convergence.
The algorithm as just described monotonically approaches a local minimum of the cost function.
Properties
Although an EM iteration does increase the observed data (i.e., marginal) likelihood function, no guarantee exists that the sequence converges to a
EM is especially useful when the likelihood is an exponential family, see Sundberg (2019, Ch. 8) for a comprehensive treatment:[16] the E step becomes the sum of expectations of sufficient statistics, and the M step involves maximizing a linear function. In such a case, it is usually possible to derive closed-form expression updates for each step, using the Sundberg formula[17] (proved and published by Rolf Sundberg, based on unpublished results of Per Martin-Löf and Anders Martin-Löf).[8][9][11][12][13][14]
The EM method was modified to compute
Other methods exist to find maximum likelihood estimates, such as
Proof of correctness
Expectation-Maximization works to improve rather than directly improving . Here it is shown that improvements to the former imply improvements to the latter.[18]
For any with non-zero probability , we can write
We take the expectation over possible values of the unknown data under the current parameter estimate by multiplying both sides by and summing (or integrating) over . The left-hand side is the expectation of a constant, so we get:
where is defined by the negated sum it is replacing. This last equation holds for every value of including ,
and subtracting this last equation from the previous equation gives
However, Gibbs' inequality tells us that , so we can conclude that
In words, choosing to improve causes to improve at least as much.
As a maximization–maximization procedure
The EM algorithm can be viewed as two alternating maximization steps, that is, as an example of coordinate descent.[19][20] Consider the function:
where q is an arbitrary probability distribution over the unobserved data z and H(q) is the entropy of the distribution q. This function can be written as
where is the conditional distribution of the unobserved data given the observed data and is the Kullback–Leibler divergence.
Then the steps in the EM algorithm may be viewed as:
- Expectation step: Choose to maximize :
- Maximization step: Choose to maximize :
Applications
- EM is frequently used for
- In psychometrics, EM is an important tool for estimating item parameters and latent abilities of item response theory models.
- With the ability to deal with missing data and observe unidentified variables, EM is becoming a useful tool to price and manage risk of a portfolio.[citation needed]
- The EM algorithm (and its faster variant computed tomography. See below for other faster variants of EM.
- In Operational Modal Analysis).
- EM is also used for inside-outside algorithm for unsupervised induction of probabilistic context-free grammars.
- In the analysis of intertrade waiting times i.e. the time between subsequent trades in shares of stock at a stock exchange the EM algorithm has proved to be very useful.[25]
Filtering and smoothing EM algorithms
A Kalman filter is typically used for on-line state estimation and a minimum-variance smoother may be employed for off-line or batch state estimation. However, these minimum-variance solutions require estimates of the state-space model parameters. EM algorithms can be used for solving joint state and parameter estimation problems.
Filtering and smoothing EM algorithms arise by repeating this two-step procedure:
- E-step
- Operate a Kalman filter or a minimum-variance smoother designed with current parameter estimates to obtain updated state estimates.
- M-step
- Use the filtered or smoothed state estimates within maximum-likelihood calculations to obtain updated parameter estimates.
Suppose that a
where are scalar output estimates calculated by a filter or a smoother from N scalar measurements . The above update can also be applied to updating a Poisson measurement noise intensity. Similarly, for a first-order auto-regressive process, an updated process noise variance estimate can be calculated by
where and are scalar state estimates calculated by a filter or a smoother. The updated model coefficient estimate is obtained via
The convergence of parameter estimates such as those above are well studied.[26][27][28][29]
Variants
A number of methods have been proposed to accelerate the sometimes slow convergence of the EM algorithm, such as those using
Parameter-expanded expectation maximization (PX-EM) algorithm often provides speed up by "us[ing] a `covariance adjustment' to correct the analysis of the M step, capitalising on extra information captured in the imputed complete data".[31]
Expectation conditional maximization (ECM) replaces each M step with a sequence of conditional maximization (CM) steps in which each parameter θi is maximized individually, conditionally on the other parameters remaining fixed.[32] Itself can be extended into the Expectation conditional maximization either (ECME) algorithm.[33]
This idea is further extended in generalized expectation maximization (GEM) algorithm, in which is sought only an increase in the objective function F for both the E step and M step as described in the As a maximization–maximization procedure section.[19] GEM is further developed in a distributed environment and shows promising results.[34]
It is also possible to consider the EM algorithm as a subclass of the MM (Majorize/Minimize or Minorize/Maximize, depending on context) algorithm,[35] and therefore use any machinery developed in the more general case.
α-EM algorithm
The Q-function used in the EM algorithm is based on the log likelihood. Therefore, it is regarded as the log-EM algorithm. The use of the log likelihood can be generalized to that of the α-log likelihood ratio. Then, the α-log likelihood ratio of the observed data can be exactly expressed as equality by using the Q-function of the α-log likelihood ratio and the α-divergence. Obtaining this Q-function is a generalized E step. Its maximization is a generalized M step. This pair is called the α-EM algorithm[36] which contains the log-EM algorithm as its subclass. Thus, the α-EM algorithm by Yasuo Matsuyama is an exact generalization of the log-EM algorithm. No computation of gradient or Hessian matrix is needed. The α-EM shows faster convergence than the log-EM algorithm by choosing an appropriate α. The α-EM algorithm leads to a faster version of the Hidden Markov model estimation algorithm α-HMM. [37]
Relation to variational Bayes methods
EM is a partially non-Bayesian, maximum likelihood method. Its final result gives a
Geometric interpretation
In information geometry, the E step and the M step are interpreted as projections under dual affine connections, called the e-connection and the m-connection; the Kullback–Leibler divergence can also be understood in these terms.
Examples
Gaussian mixture
Let be a sample of independent observations from a mixture of two multivariate normal distributions of dimension , and let be the latent variables that determine the component from which the observation originates.[20]
- and
where
- and
The aim is to estimate the unknown parameters representing the mixing value between the Gaussians and the means and covariances of each:
where the incomplete-data likelihood function is
and the complete-data likelihood function is
or
where is an indicator function and is the probability density function of a multivariate normal.
In the last equality, for each i, one indicator is equal to zero, and one indicator is equal to one. The inner sum thus reduces to one term.
E step
Given our current estimate of the parameters θ(t), the conditional distribution of the Zi is determined by
These are called the "membership probabilities", which are normally considered the output of the E step (although this is not the Q function of below).
This E step corresponds with setting up this function for Q:
The expectation of inside the sum is taken with respect to the probability density function , which might be different for each of the training set. Everything in the E step is known before the step is taken except , which is computed according to the equation at the beginning of the E step section.
This full conditional expectation does not need to be calculated in one step, because τ and μ/Σ appear in separate linear terms and can thus be maximized independently.
M step
being quadratic in form means that determining the maximizing values of is relatively straightforward. Also, , and may all be maximized independently since they all appear in separate linear terms.
To begin, consider , which has the constraint :
This has the same form as the maximum likelihood estimate for the binomial distribution, so
For the next estimates of :
This has the same form as a weighted maximum likelihood estimate for a normal distribution, so
- and
and, by symmetry,
- and
Termination
Conclude the iterative process if for below some preset threshold.
Generalization
The algorithm illustrated above can be generalized for mixtures of more than two multivariate normal distributions.
Truncated and censored regression
The EM algorithm has been implemented in the case where an underlying linear regression model exists explaining the variation of some quantity, but where the values actually observed are censored or truncated versions of those represented in the model.[38] Special cases of this model include censored or truncated observations from one normal distribution.[38]
Alternatives
EM typically converges to a local optimum, not necessarily the global optimum, with no bound on the convergence rate in general. It is possible that it can be arbitrarily poor in high dimensions and there can be an exponential number of local optima. Hence, a need exists for alternative methods for guaranteed learning, especially in the high-dimensional setting. Alternatives to EM exist with better guarantees for consistency, which are termed moment-based approaches[39] or the so-called spectral techniques[40][41][citation needed]. Moment-based approaches to learning the parameters of a probabilistic model are of increasing interest recently[when?] since they enjoy guarantees such as global convergence under certain conditions unlike EM which is often plagued by the issue of getting stuck in local optima. Algorithms with guarantees for learning can be derived for a number of important models such as mixture models, HMMs etc. For these spectral methods, no spurious local optima occur, and the true parameters can be consistently estimated under some regularity conditions[citation needed].
See also
- mixture distribution
- compound distribution
- density estimation
- Principal component analysis
- total absorption spectroscopy
- The EM algorithm can be viewed as a special case of the majorize-minimization (MM) algorithm.[42]
References
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- ^ Jeongyeol Kwon, Constantine Caramanis Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108:1727-1736, 2020.
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- ^ a b Rolf Sundberg. 1971. Maximum likelihood theory and applications for distributions generated when observing a function of an exponential family variable. Dissertation, Institute for Mathematical Statistics, Stockholm University.
- ^ MR 0443190.
- ^ See the acknowledgement by Dempster, Laird and Rubin on pages 3, 5 and 11.
- ^ a b Per Martin-Löf. 1966. Statistics from the point of view of statistical mechanics. Lecture notes, Mathematical Institute, Aarhus University. ("Sundberg formula", credited to Anders Martin-Löf).
- ^ a b Per Martin-Löf. 1970. Statistiska Modeller (Statistical Models): Anteckningar från seminarier läsåret 1969–1970 (Lecture notes 1969-1970), with the assistance of Rolf Sundberg. Stockholm University.
- ^ G. A. Barnard, O. Barndorff-Nielsen, J. D. Kalbfleisch and G. Raschand a reply by the author. Proceedings of Conference on Foundational Questions in Statistical Inference (Aarhus, 1973), pp. 1–42. Memoirs, No. 1, Dept. Theoret. Statist., Inst. Math., Univ. Aarhus, Aarhus, 1974.
- ^ a b Martin-Löf, Per (1974). "The notion of redundancy and its use as a quantitative measure of the discrepancy between a statistical hypothesis and a set of observational data". Scand. J. Statist. 1 (1): 3–18.
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Wu, C. F. Jeff (Mar 1983). "On the Convergence Properties of the EM Algorithm". MR 0684867.
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- ^ Hunter DR and Lange K (2004), A Tutorial on MM Algorithms, The American Statistician, 58: 30–37
- ^ Matsuyama, Yasuo (2003). "The α-EM algorithm: Surrogate likelihood maximization using α-logarithmic information measures". IEEE Transactions on Information Theory. 49 (3): 692–706. .
- ^ Matsuyama, Yasuo (2011). "Hidden Markov model estimation based on alpha-EM algorithm: Discrete and continuous alpha-HMMs". International Joint Conference on Neural Networks: 808–816.
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- ^ Shaban, Amirreza; Mehrdad, Farajtabar; Bo, Xie; Le, Song; Byron, Boots (2015). "Learning Latent Variable Models by Improving Spectral Solutions with Exterior Point Method" (PDF). UAI: 792–801. Archived from the original (PDF) on 2016-12-24. Retrieved 2019-06-12.
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Further reading
- Hogg, Robert; McKean, Joseph; Craig, Allen (2005). Introduction to Mathematical Statistics. Upper Saddle River, NJ: Pearson Prentice Hall. pp. 359–364.
- ) gives an easier explanation of EM algorithm as to lowerbound maximization.
- ISBN 978-0-387-31073-2.
- Gupta, M. R.; Chen, Y. (2010). "Theory and Use of the EM Algorithm". Foundations and Trends in Signal Processing. 4 (3): 223–296. . A well-written short book on EM, including detailed derivation of EM for GMMs, HMMs, and Dirichlet.
- Bilmes, Jeff (1998). "A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models". ) includes a simplified derivation of the EM equations for Gaussian Mixtures and Gaussian Mixture Hidden Markov Models.
- McLachlan, Geoffrey J.; Krishnan, Thriyambakam (2008). The EM Algorithm and Extensions (2nd ed.). Hoboken: Wiley. ISBN 978-0-471-20170-0.
External links
- Various 1D, 2D and 3D demonstrations of EM together with Mixture Modeling are provided as part of the paired SOCRactivities and applets. These applets and activities show empirically the properties of the EM algorithm for parameter estimation in diverse settings.
- Class hierarchy in C++ (GPL) including Gaussian Mixtures
- The on-line textbook: Information Theory, Inference, and Learning Algorithms, by David J.C. MacKayincludes simple examples of the EM algorithm such as clustering using the soft k-means algorithm, and emphasizes the variational view of the EM algorithm, as described in Chapter 33.7 of version 7.2 (fourth edition).
- Variational Algorithms for Approximate Bayesian Inference, by M. J. Beal includes comparisons of EM to Variational Bayesian EM and derivations of several models including Variational Bayesian HMMs (chapters).
- The Expectation Maximization Algorithm: A short tutorial, A self-contained derivation of the EM Algorithm by Sean Borman.
- The EM Algorithm, by Xiaojin Zhu.
- EM algorithm and variants: an informal tutorial by Alexis Roche. A concise and very clear description of EM and many interesting variants.