# On The Chain Rule Optimal Transport Distance

We define a novel class of distances between statistical multivariate distributions by solving an optimal transportation problem on their marginal densities with respect to a ground distance defined on their conditional densities. By using the chain rule factorization of probabilities, we show how to perform optimal transport on a ground space being an information-geometric manifold of conditional probabilities. We prove that this new distance is a metric whenever the chosen ground distance is a metric. Our distance generalizes both the Wasserstein distances between point sets and a recently introduced metric distance between statistical mixtures. As a first application of this Chain Rule Optimal Transport (CROT) distance, we show that the ground distance between statistical mixtures is upper bounded by this optimal transport distance, whenever the ground distance is joint convex. We report on our experiments which quantify the tightness of the CROT distance for the total variation distance and a square root generalization of the Jensen-Shannon divergence between mixtures.

## Authors

• 53 publications
• 45 publications
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• ### Training Generative Networks with general Optimal Transport distances

We propose a new algorithm that uses an auxiliary Neural Network to calc...
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• ### Optimal Transport to a Variety

We study the problem of minimizing the Wasserstein distance between a pr...
09/25/2019 ∙ by T. Ö. Çelik, et al. ∙ 0

• ### Ground Metric Learning on Graphs

Optimal transport (OT) distances between probability distributions are p...
11/08/2019 ∙ by Matthieu Heitz, et al. ∙ 83

• ### Multilevel Optimal Transport: a Fast Approximation of Wasserstein-1 distances

We propose a fast algorithm for the calculation of the Wasserstein-1 dis...
09/29/2018 ∙ by Jialin Liu, et al. ∙ 1

• ### A Distance for HMMs based on Aggregated Wasserstein Metric and State Registration

We propose a framework, named Aggregated Wasserstein, for computing a di...
08/05/2016 ∙ by Yukun Chen, et al. ∙ 0

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## 1 Introduction

Calculating (dis)similarities between statistical mixtures is a core primitive often met in statistics, machine learning, signal processing, and information fusion

[3] among others. However, the usual information-theoretic Kullback-Leibler (KL) divergence (as known as relative entropy) or the -divergences between statistical mixtures [28] do not admit closed-form formula, and is in practice approximated by costly Monte Carlo stochastic integration [28].

To tackle this computational tractability problem, two research directions have been considered in the literature: The first line of research consists in proposing some distances between mixtures that yield closed-form formula [23] (e.g., the Cauchy-Schwarz divergence or the Jensen quadratic Rényi divergence). The second line of research consists in lower and upper bounding the -divergences between mixtures [28]. This is tricky when considering bounded divergences like the Total Variation (TV) distance or the Jensen-Shannon (JS) divergence that are upper bounded by and , respectively.

When dealing with probability densities, two main classes of statistical distances have been widely studied in the literature:

1. The Information-Geometric (IG) invariant -divergences [1] (characterized as the class of separable distances), and

2. The Wasserstein distances of Optimal Transport (OT) [37] which can be computationally accelerated using entropy regularization [4, 10] (Sinkhorn divergence).

In general, computing closed-form formula for the OT between parametric distributions is difficult. A closed-form formula is known for elliptical distributions [8]

(that includes the multivariate Gaussian distributions), and the OT of multivariate continuous distributions can be calculated from the OT of their copulas

[13].

The geometry induced by the distance is different in these two OT/IG cases. For example, consider location-scale families (or multivariate elliptical distributions):

1. For OT, the distance between any two members admit the same closed-form formula [8]

(depending only on the mean and variance parameters, not on the type of location-scale family). The OT geometry of Gaussian distributions has positive curvature

[41].

2. For any -divergence, the information-geometric manifold has negative curvature [18]

(hyperbolic geometry). It is known that for the Kullback-Leibler divergence, the manifold of mixtures with prescribed components is dually flat, and admits therefore an equivalent Bregman divergence

[25].

In this paper, we build on the seminal work of Liu and Huang [21]

that proposed a novel family of statistical distances for statistical mixtures by solving linear programs between

[21]

component weights of mixtures where the elementary distance between any two mixtures is prescribed. They proved that their distance between mixtures (that we term MCOT distance for Mixture Component Optimal Transport) is a metric whenever the elementary distance between mixture components is a metric. This framework also applies to semi-parametric mixtures obtained from Kernel Density Estimators

[38] (KDEs).

We describe our main contributions as follows:

• We define the Chain Rule Optimal Transport (CROT) distance in Definition 1, and prove that it yields a metric whenever the distance between conditional distributions is a metric in §2.2 (Theorem 3). The CROT distance extends the Wasserstein/EMD distances and the MCOT distance between statistical mixtures. We further sketch show how to build recursively hierarchical families of CROT distances.

• We report a novel generic upper bound for statistical distances between mixtures [29] using CROT distances in §3 (Theorem 5) whenever the ground distance is joint convex.

• In §4, experiments highlight quantitatively the upper bound performance of the CROT distance for bounding the total variation distance and a generalization of the square root of the Jensen-Shannon distance.

## 2 The Chain Rule Optimal Transport (CROT) distance

### 2.1 Definition

We define a novel class of distances between statistical multivariate distributions. Recall the basic chain rule

factorization of a joint probability distribution

:

 p(x,y)=p(y)p(x|y),

where probability is called the marginal probability, and probability is termed the conditional probability. Let and denote the manifolds of marginal probability densities and conditional probability densities, respectively.

For example, for latent models like statistical mixtures or hidden Markov models

[42, 39], plays the role of the observed variable while denotes the hidden variable [9] (unobserved so that inference has to tackle incomplete data, say, using the EM algorithm [6]).

First, we state the generic definition of the Chain Rule Optimal Transport

distance between joint distributions

and (with ) as follows:

###### Definition 1 (CROT distance).

Given two multivariate distributions and , we define the Chain Rule Optimal Transport (CROT) as follows:

 Hδ(p,q) := infrEr(y,z)[δ(p(x|y),q(x|z))], (1) = infr∫r(y,z)δ(p(x|y),q(x|z))dydz, (2)

where is a ground distance defined on conditional density manifold (e.g., the Total Variation — TV), and (set of all probability measures on with marginals and ) satisfying the following constraint:

 ∫r(y,z)dz=p(y),∫r(y,z)dy=q(z), (3)

When the ground distance is clear from the context, we write for a shortcut of . Since and since is a feasible transport solution, we get the following upper bounds:

###### Property 2 (Upper bounds).

The CROT is upper bounded by

 Hδ(p,q)≤∫y∫zp(y)q(z)δ(p(x|y),q(x|z))dydz≤maxy,zδ(p(x|y),q(x|z)).

Figure 1 illustrates the principle of the CROT distance. Another complementary motivation when dealing with statistical mixtures ispresented in §3

Let us notice that the CROT distance generalizes two distances met in the literature:

###### Remark 2.1 (CROT generalizes Wasserstein/EMD).

In the case that (Dirac distributions), we recover the Wasserstein distance [41] between point sets (or Earth Mover Distance [36]), where is the ground metric distance.

###### Remark 2.2 (CROT generalizes MCOT).

When both and are both (finite) categorical distributions, we recover the distance formerly defined in [21] that we term the MCOT distance in the remainder (for Mixture Component Optimal Transport).

### 2.2 CROT is a metric when the ground distance is a metric

###### Theorem 3 (CROT metric).

is a metric whenever is a metric.

###### Proof.

We prove that satisfies the following axioms of metric distances:

Non-negativity.

As , we have .

Law of indiscernibles.

If , as is a metric, then the density is concentrated on the region in . We therefore have

 (4)
Symmetry.
 Hδ(p,q) (5) =Hδ(q,p) (6)

where s.t. and .

Triangle inequality.

The proof of the triangle inequality is not straightforward.

 Hδ(p1,p2)+Hδ(p2,p3) =infr1Er1(y,z)δ(p1(x|y),p2(x|z))+infr2Er2(y,z)δ(p2(x|y),p3(x|z)) =infrEs(y1,y2,z)(δ(p1(x|y1),p2(x|z))+δ(p2(x|y2),p3(x|z))) ≥infrEs(y1,y2,z)δ(p1(x|y1),p3(x|z)) ≥infrEr(y,z)δ(p1(x|y),p3(x|z)), (7)

where denotes the set of all probability measures on with marginals , and . ∎

## 3 CROT for statistical mixtures and Sinkhorn CROT

Consider two finite statistical mixtures and , not necessarily homogeneous nor of the same type. Let . The Mixture Component Optimal Transport (MCOT) distance proposed in [21] amounts to solve a Linear Program (LP) with the following objective function to minimize:

 Hδ(p,q)=k1∑i=1k2∑j=1wijδ(pi,qj), (8)

satisfying the following constraints:

 wij ≥ 0,∀i∈[k1],j∈[k2] (9) k2∑l=1wil = αi,∀i∈[k1] (10) k1∑l=1wlj = βj,∀j∈[k2]. (11)

By defining to be set of non-negative matrices with and (transport polytope [5]), we get the equivalent compact definition of MCOT/CROT:

 Hδ(m1:m2)=minW∈U(α,β)k1∑i=1k2∑j=1wijδ(pi,qj). (12)

When the ground distance is asymmetric, we shall use the ’:’ notation instead of the ’,’ notation for separating arguments.

In general, the LP problem (with variables and inequalities, equalities whom are independent) delivers an optimal soft assignment of mixture components with exactly nonzero coefficients111A LP in -dimensions has its solution located at a vertex of a polytope, described by the intersection of hyperplanes (linear constraints). in matrix . The complexity of linear programming in variables with bits using Karmarkar’s interior point methods is polynomial, in  [19].

Observe that we necessarily have:

 maxj∈[k2]wij≥αik2,

and similarly that:

 maxi∈[k1]wij≥βjk1.

Note that since where denotes the Krönecker symbol: iff , and otherwise.

We can interpret MCOT as a discrete optimal transport between (non-embedded) histograms. When , the transport polytope is the polyhedral set of non-negative matrices:

 U(α,β)={P∈Rd×d+ : P1d=α,P⊤1d=β},

and

 Hδ(m1:m2)=minP∈U(α,β)⟨P,W⟩,

where is the Fröbenius inner product of matrices, and the matrix trace. This OT can be calculated using the network simplex in time.

Cuturi [5] showed how to relax the objective function in order to get fast calculation using the Sinkhorn divergence:

 Sδ(m1:m2)=minP∈Uλ(α,β)⟨P,W⟩, (13)

where . The KL divergence between two matrices and is defined by

 KL(M:M′):=∑i,jmi,jlogmi,jm′i,j,

with the convention that . The Sinkhorn divergence is calculated using the equivalent dual Sinkhorn divergence by using matrix scaling algorithms (e.g., the Sinkhorn-Knopp algorithm).

Because the minimization is performed on , we have

 Hδ(m1:m2)≤Sδ(m1:m2). (14)

### 3.1 Upper bounding statistical distances between mixtures with CROT

First, let us report the basic upper bounds for MCOT mentioned earlier in Property 2. The objective function is upper bounded by:

 H(m1,m2)≤k1∑i=1k2∑j=1αiβjδ(pi,qj)≤maxi∈[k1],j∈[k2]δ(pi,qj). (15)

Now, when the conditional density distance is separate convex (i.e., meaning convex in both arguments), we get the following Separate Convexity Upper Bound (SCUB):

 (SCUB):δ(m1:m2)≤k1∑i=1k2∑j=1αiβjδ(pi:qj). (16)

For example, norm-induced distances or -divergences [26] are separate convex distances.

For the particular KL divergence

 KL(p:q):=∫p(x)logp(x)q(x)dx,

and when , we get the following upper bound using the log-sum inequality [7, 27]:

 KL(m1:m2)≤KL(α:β)+k∑i=1αiKL(pi:qi), (17)

Since this holds for any permutation of of mixture components, we can tight this upper bound by minimizing over all permutations:

 KL(m1:m2)≤minσKL(α:σ(β))+k∑i=1αiKL(pi:σ(qi)). (18)

The best permutation can be computed using the Hungarian cubic time algorithm [40, 35, 15, 14] (with cost matrix , and with ).

Now, let us further rewrite with , and with . That is, we can interpret and as mixtures of (redundant) components and , and apply the upper bound of Eq. 17 for the “best split” of matching mixture components :

 KL(m1:m2)≤minw∈U(α,β)k1∑i=1k2∑j=1wi,jlogwi,jw′j,i+k1∑i=1k2∑j=1wijKL(pi:qj), (19)

Let

 O(m1:m2)=minw∈U(α,β)k1∑i=1k2∑j=1wi,jlogwi,jw′j,i+k1∑i=1k2∑j=1wijKL(pi:qj). (20)

Then it follows that

 KL(m1:m2)≤O(m1:m2)≤k1∑i=1k2∑j=1wi,jlogwi,jw′j,i+HKL(m1,m2). (21)

Thus CROT allows to upper bound the KL divergence between mixtures. The technique of rewriting mixtures as mixtures of redundant components bears some resemblance with the variational upper bound on the KL between mixtures proposed in [16] that requires to iterate until convergence an update of the variational upper bound.

In fact, the CROT distance provides a good upper bound on the distance between mixtures provided the base distance is joint convex [2, 33].

###### Definition 4 (Joint convex distance).

A distance is joint convex if and only if

 D((p1p2)α:(q1q2)α)≤(D(p1:p2)D(p2:q2))α,∀α∈[0,1],

where .

The -divergences (for a convex generator satisfying ) are joint convex distances [31]. For mixtures with same weights but different component basis and a joint convex distance (e.g., KL), we get .

###### Theorem 5 (Upper Bound on Joint Convex Mixture Distance (UBJCMD)).

Let and be two finite mixtures, and any joint convex statistical base distance. Then CROT upper bounds the distance between mixtures:

 (JCUB):δ(m1:m2)≤Hδ(m1:m2). (22)
###### Proof.
 δ(m1:m2) = δ(k1∑i=1αipi,k2∑j=1βjqj) = δ(k1∑i=1k2∑j=1wi,jpi,j:k1∑i=1k2∑j=1wi,jqi,j) ≤ k1∑i=1k2∑j=1wi,jδ(pi,j:qi,j), ≤ k1∑i=1k2∑j=1wi,jδ(pi:qj)=:Hδ(m1,m2).

Notice that for asymmetric base distance .

Conversely, CROT yields a lower bound for joint concave distances (e.g., fidelity in quantum computing [30]).

Figure 2 illustrates the CROT distance between statistical mixtures (not having the same number of components).

## 4 Experiments

### 4.1 Total Variation distance

Since is a metric -divergence [17] bounded in , so is MCOT. The closed-form formula for the total variation between univariate Gaussian distributions is reported in [24]

(using the erf function), and the other formula for the total variation between Rayleigh distributions and Gamma distributions is given in

[29].

Figure 2(a) illustrates the performances of the various lower/upper bounds on the total variation between mixtures of Gaussian, Gamma, and Rayleigh distributions with respect to the true value which is estimated using Monte Carlo samplings.

The acronyms of the various bounds are as follows:

• CELB: Combinatorial Envelope Lower Bound [28] (applies only for 1D mixtures)

• CEUB: Combinatorial Envelope Upper Bound [28] (applies only for 1D mixtures)

• CGQLB: Coarse-Grained Quantization Lower Bound [28] for bins (applies only for -divergences that satisfy the information monotonicity property)

• CROT: Chain Rule Optimal Transport (this paper)

• Sinkhorn CROT: Entropy-regularized CROT [5] , with and (for convergence of the Skinhorn-Knopp iterative matrix scaling algorithm).

Next, we consider the renown MNIST handwritten digit database [20]: A dataset of 70000 handwritten digit grey images.

We learn GMMs composed of multivariate Gaussian distributions with a diagonal covariance matrix from this MNIST database using PCA (dimension reduction from original dimension

to reduced dimension ) as explained in the caption of Table 1

. We used the Expectation-Maximization (EM) algorithm implementation of

scikit-learn [32].

We approximate the TV between -dimensional GMMs using Monte Carlo by performing stochastic integration of the following integrals: Let ,

 TV(p,q):=12∫|p(x)−q(x)|dx =12∫r(x)∣∣∣p(x)r(x)−q(x)r(x)∣∣∣dx =14∫p(x)∣∣∣p(x)−q(x)r(x)∣∣∣dx+14∫q(x)∣∣∣p(x)−q(x)r(x)∣∣∣dx.

Furthermore, we have:

 p(x)−q(x)r(x)=2p(x)−q(x)p(x)+q(x)=2p(x)q(x)−1p(x)q(x)+1.

The results are obtained using POT [11] (Python Optimal Transport).

Our experiments yield the following observations: As the sample size decreases, the TV distances between GMMs turn larger because the GMMs are pulled towards the two different empirical distributions. As the dimension increases, TV increases because in a high dimensional space the GMM components are less likely to overlap. We check that CROT-TV is an upper bound of TV. We verify that Sinkhorn divergences are upper bounds of CROT.

### 4.2 Square root of the symmetric α-Jensen-Shannon divergence

TV is bounded in which makes it difficult to appreciate the quality of the CROT upper bounds in general. We shall consider a different parametric distance that is upper bounded by an arbitrary bound: .

It is well known that the square root of the Jensen-Shannon divergence is a metric [12] (satisfying the triangle inequality). In [22], a generalization of the Jensen-Shannon divergence was proposed, given by

 JSα(p:q):=KL(p:(pq)α)+KL(q:(pq)α), (23)

where . unifies (twice) the Jensen-Shannon divergence (obtained when ) with the Jeffreys divergence ([22]

. A nice property is that the skew

-divergence is upper bounded as follows:

 KL(p:(pq)α)≤∫plogp(1−α)p≤−log(1−α)

for , so that for .

Thus, we have the square root of the symmetrized -divergence that is upper bounded by

 √JSα(p:q)≤Cα=√−2log(1−α).

However, is not a metric in general [31]. Indeed, in the extreme case of , it is known that any positive power of the Jeffreys divergence does not yield a metric.

Observe that is a -divergence since is a -divergence for the generator , and we have . Since for , it follows that the -generator for the divergence is:

 fJSα(u)=−log((1−α)+αu)−log(α+1−αu). (24)

Figure 2(b) displays the experimental results obtained for the -JS divergences.

## 5 Conclusion and perspectives

In this work, we defined the generic Chain Rule Optimal Transport (CROT) distance (Definition 1) for a ground distance that encompasses the Wasserstein distance between point sets (Earth Mover Distance [36]) and the Mixture Component Optimal Transport (MCOT) distance [21], and proved that is a metric whenever is a metric (Theorem 3). We then dealt with statistical mixtures, and showed that (Theorem 5) whenever is joint convex. This holds in particular for statistical -divergences :

 HIf(m1:m2)≥If(m1:m2).

We also considered the smoothened Sinkhorn CROT distance for fast calculations of via matrix scaling algorithms (Sinkhorn-Knopp algorithm), with .

There are many venues to explore for further research. For example, we may consider infinite Gaussian mixtures [34], the chain rule factorization for

-variate densities: This gives rise to a hierarchy of CROT distances. Another direction is to explore the use of the CROT distance in deep learning.

The smooth (dual) Sinkhorn divergence has also been shown experimentally (MNIST classification) to improve over the EMD in applications [5]. It would be also interesting to consider the Sinkhorn CROT vs CROT in applications [21] that deal with mixtures of features.

## Acknowledgments

Frank Nielsen thanks Steve Huntsman for pointing out reference [21] to his attention.

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