Discrete context models are well established and successful tools with which to model or compress natural language text. These models predict the next letter, conditioned on the last few letters seen.
When the data are continuous, as in speech recognition or image processing, the situation is more complicated because one cannot rely upon exact context matches. As a result, context is typically either ignored, resulting in weak models, or it is incorporated in a way that is in some sense technically unsatisfactory, i.e. the contexts overlap so that the future is in effect used to predict the past.
This chapter discusses the learning of context models for continuous problem domains within a strict probabilistic framework. The probability of a time series, or of a static object such as an image, is expressed as a product of conditional probabilities. Each term predicts a never-before-seen part of the observation conditioned on earlier-seen portions. Models of this form are referred to in the literature as causal.
Our focus is on such models where each term arises by conditionalizing a mixture of primitive models, e.g. a conditionalized mixture of normal densities. We show that reestimation of such a model's parameters may be reduced to two simpler tasks. In the case of normal densities one of these is trivial, and the other may be approached the emphasis parameterization introduced in the previous chapter.
Now in the case of normal mixtures one might instead use a conventional gradient descent approach and a standard parameterization. Our contribution is the identification of a particularly simple, generic, and in some sense natural alternative.
In the case of a time series (e.g. a speech signal) the objective is usually to learn the parameters of a single conditional model that is then applied repeatedly to evaluate the probability of a series. For static objects such as images of handwritten digits, one might instead associate a different conditional model with each pixel position. The image's probability is then the product of the predictions made by these distinct models.
The original motivation for this work was in fact the learning of improved causal models for images - in particular of handwritten symbols. The developments of this chapter may form the basis for future experiments in this direction.
Given a collection of pairs where and , our objective is to maximize over :
We begin by observing that if the model is a single multivariate normal density, then this problem may be solved by instead optimizing the joint probability:
So the case of a single normal density is in a sense uninteresting. We remark that linear predictive speech coding (LPC) may be viewed as such a model where .
In general these simple conditional forms arising from a single normal density form a prediction based on a linear transformation of the conditioning information . As such their expressive power is limited. In particular they cannot deal with modality. For example, the population of values may separate into two obvious clusters, each leading to very different predictions for . A simple linear predictor cannot cope with this situation and will instead make a single blended prediction.
By moving from single densities to mixture densities, nonlinear predictions result and modality can be addressed.
A general mixture may be written
to describe the joint density . Provided that each component has an associated conditional form we may then write:
Such conditionalized mixture forms can capture modality because their mixing coefficients are not constant. That is, they depend on the context and the mixture stochastically selects a component based on the context.
If each component is a normal density with fixed parameters, the IMM technique for adaptive estimation and tracking results [BSL93,BBS88]. From this perspective, our interest is in recovering the model's parameters from observations.
For a single normal density we have seen that an optimal conditional model may be formed by first forming an optimal joint model, and then conditionalizing - and that the optimal joint model is specified by the sample mean and covariance.
Building an optimal joint model for a mixture of normal densities is a non-trivial and heavily studied problem. The most popular approach is Expectation Maximization (EM) which is discussed in earlier chapters and is an iterative technique for climbing to a locally optimal model.
But the situation is worse yet for conditionalized mixture forms because it turns out that conditionalizing an optimal joint mixture model, need not result in an optimal conditional model.
If the underlying joint density is exactly a normal mixture of components, then the optimal conditional density is of course just the conditionalized form the joint density (by direct calculation). But given a finite sample, and especially one that is explained poorly by a normal mixture of components, the optimal conditional model will in general be different.
To see this we offer the following argument sketch: suppose that and are independent with explained perfectly by a element mixture. Then Now consider equation 22. The terms are constant in an optimal model for but instead are conditioned on in the equation. By modeling the joint density it will seldom be the case that these terms are constant whence the resulting model is suboptimal. That is, in the case of independence, the context should be ignored.
The next section further reveals the role of these a posteriori class functions in the overall optimization. An example serves to illustrate that the ML estimate does not necessarily optimize a posteriori class probabilities. Consider figure 14(a) which depicts four points along the x-axis, two labeled white and two black. These colors correspond to two classes and . The functions sketched above them represent the ML normal density associated with each class. Notice the mean of each is centered at the midpoint between each pair of points, and that the variance is considerable. If one computes, for example, the a posteriori probability based on the two normal densities shown, the result will certainly exceed but clearly falls well short of unity. If the densities are replaced with the more peaked forms of the figure's part (b), all a posteriori probabilities are in closer correspondence to the point colors. This is true despite the fact that their means have drifted away from the original midpoint locations. This illustrates that ML optimization of a labeled dataset need not optimize a posteriori class probabilities.
Figure 14 also illustrates the general concept of emphasis introduced in the previous chapter since the densities of part (b) may be generated by placing more weight on points and and computing the weighted mean and variance. As the weight on these outer points is increased the densities approach a pair of vertical pulses (also depicted) and the a posteriori probabilities approach the ideal values of zero or one in correspondence with each point's color.
This section demonstrates how one may separate the parameter-learning task for conditional mixtures, into two simpler problems. This is accomplished using the mathematical machinery of EM combined with the introduction of new parameters.
Let denote the random variable being predicted/modeled, and a random variable which conditions the prediction. To simplify our notation we will generally use the single function to denote both probability densities and probabilities; as well as related marginals and conditional forms. In all cases it should be possible to distinguish different functions by context. We begin by defining the central problem of this chapter:
where the values and are members of some (possibly finite) measure spaces.
We remark that may be any non-negative function since it may be normalized to form a probability function without affecting the problem at hand. The MRCE problem as one step of an iterative approach since it is not generally possible to solve directly for even a local maximum of .
Our interest is in parameterized conditional densities which arise from finite mixture densities of the form:
where is a hidden discrete random variable which corresponds to selection of a component of the mixture. We then write:
where it assumed that with . Were this not the case, we could make it so by duplicating any common parameters. The resulting parameterized density includes the original pdf as a special case. So except for the matter of added model complexity, our assumption that is made without harm6.1. The generation of a pair may be thought of as a two stage process. First, a pair is chosen according to Second, is generated according to and conditioned on the earlier choice of . Now from which we easily derive the conditional prediction formula:
Thus the conditional form required by the MRCE problem arises naturally from the joint mixture density. Next, , and as before we can without weakening the model, assume that is made up of the marginal values which we will sometimes denote as just , and independently parameterized densities . We will omit the subscript on since it is clear from the conditioning on what density we are referring to. We remark that the same result may be reached by starting with the more common definition of a mixture density:
where , the are nonnegative, and each is a parameterized probability density function with . We prefer our presentation however since it seems more natural given the structure of later derivations.
Now suppose that is an independent sample of unlabeled observations where denotes and denotes . Our approach in this chapter is that of maximum-likelihood estimation6.2, and we are therefore interested in finding a value for which maximizes , i.e. . Such a value need not exist in general due to degenerate cases such as zero variance distributions, but we will not consider this complicating detail here. Since we can equally well maximize the log-likelihood, we have the following:
The definitions above and much of the development that follows may be generalized to the case where and or are measure spaces; in which case the summations become integrals. We will however present the discrete case since it corresponds to our original problem of parameter estimation given a finite sample.
We now show that the mixture-MRCE problem may be approached by reducing it to an instance of itself with particular structure, and multiple independent ordinary (nonmixture) MRCE problems. The ordinary MRCE problems are simpler because each concerns only a single component of the original mixture and excludes entirely the mixing coefficients . The mixture-MRCE subproblem is simpler because it excludes the of the original problem, i.e. it concerns only the model .
Proof: Using the same steps used derive the well-known EM algorithm, we have:
Reordering the summations in Eq-23 and grouping yields:
which after normalization of the bracketed term is easily recognized as an instance of the mixture-MRCE problem. The second term Eq-24 making up may be similarly transformed to yield:
which is just independent instances of the simple MRCE problem. Finally, observe that Eq-6.2 and Eq-6.2 are independent of one another since the first is an optimization problem over and the second over - completing our proof.
Notice that if contains a single value, the complexities introduced by the conditional form of the problem vanish and one is left with the standard EM setting in which the mixture-MRCE subproblem is trivially maximized. Also, the conditional estimation problem becomes a plain estimation problem.
For normal densities the conditional estimation problems of Eq-24 are easily solved since the optimal conditional normal density is obtained by finding the optimal joint density and then conditionalizing.
The MRCE problem of Eq-23 is however much more difficult. It, or for that matter the entire problem, might be addressed by gradient descent where the covariance matrices have been reparameterized to ensure positive definiteness. The purpose of this chapter is to describe an alternative approach.
Using the emphasis reparameterization results of the previous chapter, we may (except for degenerate cases) express a posteriori class behavior by reweighting the available samples.
So in particular Eq-23 may be approached in this way. That is, the may be reweighted until the term is maximized. This amounts to a reparameterization of the problem in terms of these weights rather than in terms of means, covariance matrices, and mixing coefficients.
The fact that for normal densities one may pass easily from such a weight set, to an ML traditional parameter set, is essential to the approach. That it is capable of expressing the solution is also a special characteristic of normal densities (see previous chapter).
But in practice one might wish to use this reparameterization (without proof of expressiveness) for other densities for which an ML estimate of a weighted sample set is readily available. Its virtue is that the particular density may then be viewed as a data type referred to by a single optimization algorithm.
A particularly simple numerical prescription for optimization is that of Powell (see [Act70,Bre73,PFTV88]). Speed of convergence is traded for simplicity in that no gradient computation is required. Emphasis reparameterization does however introduce one wrinkle. The number of parameters can easily exceed the degrees of freedom in the underlying problem. In the case of normal mixtures, the number of natural parameters (i.e. mean vector values and covariance matrix entries) may be far less than the number of training samples available. In this case we propose to modify Powell's method to use an initial set of random directions with size matching the number of native parameters.
The result is a simple and highly general approach to estimating the parameters of conditional mixtures. That the approach can express the globally optimal parameter set has only been established for normal mixtures, but we expect that approach may nevertheless work well in other settings.
The conditional entropy minimization problem can express the traditional problem of supervised learning given boolean with . That is, each datapoint has an attached label. The MRCE problem is then to minimize the bits required to express the labels, given the data. Our definition of MRCE places no restriction, other than non-negativity, on the . In this section we motivate the form of our definition by showing that these weights have a natural stochastic interpretation.
Imagine a random process where each trial consists of drawing a label from for each of the points . The outcome of each trial is represented by boolean random variables defined to be if is observed to have label , and otherwise. No independence assumptions are made. The trials might be time dependent; and within a trial, the labels might be dependent in some complex way. Each trial produces a labeled dataset with likelihood:
Denoting by the logarithm of this likelihood, a straightforward consequence of the linearity of expectations is then that:
where the expectation is over trials of the random labeling process. The key feature of this expression is that the expectation of each labeled dataset's log likelihood, is independent of the exact nature of the labeling process; depending only on the individual expectations . The significance of this independence, is that we can optimize with respect to , where the only knowledge we have regarding the labeling process is the set of terms . Now since only a single label is drawn for each point. So as well. These terms are therefore the probability of observing label given point and correspond to the term in Eq-23. Because of the argument above, we think of the weights as stochastic labels attached to each point.
In the setting above, each trial can be thought of as drawing a sample of size consisting of exactly each time. This is just one example of a process which draws sample of size , from the with equal probability . An argument essentially the same as that above can then be made for a process in which each trial draws a single data point from and then a single label for that point. By repeated trials, the sample of size is built. The result is that given only the probability of drawing each , and the , we can optimize our expected log likelihood for a series of samples, independent of the exact nature of the process.
Now an equivalent problem results if our matrix of weights is scaled by a constant so that the sum of its entries is , so we will assume that this is the case. It can then be thought of a joint probability density on so that entry may be expressed as a product . More formally, may be expressed as a diagonal matrix with trace representing , times a row stochastic matrix representing .
In summary, an instance of the MRCE problem may be thought of as consisting of and the stochastic knowledge in the two matrices above. The significance of its solution is that it represents a minimum not just over parameter values, but over all processes consistent with this knowledge. If our objective were to maximize expected likelihood rather than expected log likelihood, this would not be true. Thus beyond the obvious mathematical convenience of working with log likelihood, there is a deeper benefit to doing so. Everything we've said is true in a far more general sense for arbitrary density and sets and , where we focus on a series of samples and have the likelihood:
but we will not repeat the argument above at this slightly more abstract level.
Instead we turn to a concrete example loosely motivated by the problem of portfolio optimization as framed in [Cov84] and discussed in chapter 2, in order to illustrate the mathematical point above.
Suppose that one may draw vectors from at random according to some hidden distribution , and that the one thing known about this distribution is that only two values and are ever drawn in each component position, and that they occur with equal probability in each position. That is, all single variable marginals are known.
Now focus on the product of a vector's components. Our expectation of the of this product's value is zero because the product becomes a sum of logs, and because of linearity of expectations.
Of significance is that the expectation is zero, independent of the nature of . By contrast, our expectation of the product's value (without taking the logarithm) is highly distribution dependent. For example, if the distribution generates a vector of 's half of the time and a vector of 's the rest of the time, the resulting expectation is and is immense.
The fascinating observation regarding optimizing log-likelihood as opposed to likelihood, is that on the one hand the optimization is over a huge space of hidden distributions, but on the other hand the optimization ignores information in those distributions that can significantly affect the expected likelihood.
We conclude by identifying and briefly discussing a number of potential applications for conditional normal mixture models (CNM).
As mentioned earlier, our starting point was handwriting recognition and in particular the problem of offline digit classification. The space of digit images is modeled as a mixture of models, one for each digit. We suggest that each digit's model itself consist of a mixture of models intended to capture the sometimes very different styles used to represent the same numeral. Each digit style model would then consist of a product of CNMs, one for each pixel position, with conditioning on some subset of the earlier-seen pixels. The use of CNMs in this way promises to better model the variation that exists within a single style. The final model may be used to implement a traditional Bayesian classifier. The introduction of discriminative training into this framework is an interesting area for future work.
The same approach might be used in machine vision to build highly general statistical models for particular objects based on a set of training images. A classifier may then build as for handwritten digits. Applied to images CNMs might also find application in image restoration or perhaps in the area of digital water-marking.
Our development in chapter 2 showed that Baum-Welch/EM may be used to estimate the parameters of non-causal models as well. In many applications, including some of those mentioned above, a non-causal model may suffice. For images this corresponds to the modeling of a pixel using a context that entirely surrounds it. Baum-Welch/EM is certainly easier to implement than the approach of this chapter and may therefore represent the technique of choice when implementing non-causal CNM models.
Finally we point out that while this chapter has focused on ML estimation, techniques similar to those of chapter 2 might be developed to implement alternative estimation criteria.