# 2005 AUnifyingViewofSparseApproximat

## Quotes

### Abstract

We provide a new unifying view, including all existing proper probabilistic sparse approximations for Gaussian process regression. Our approach relies on expressing the effective prior which the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically justified ranking of the closeness of the known approximations to the corresponding full GPs. Finally we point directly to designs of new better sparse approximations, combining the best of the existing strategies, within attractive computational constraints.

### Introduction

Regression models based on Gaussian processes (GPs) are simple to implement, flexible, fully probabilistic models, and thus a powerful tool in many areas of application. Their main limitation is that memory requirements and computational demands grow as the square and cube respectively, of the number of training cases n, effectively limiting a direct implementation to problems with at most a few thousand cases. To overcome the computational limitations numerous authors have recently suggested a wealth of sparse approximations. Common to all these approximation schemes is that only a subset of the latent variables are treated exactly, and the remaining variables are given some approximate, but computationally cheaper treatment. However, the published algorithms have widely different motivations, emphasis and exposition, so it is difficult to get an overview (see [[Rasmussen and Williams, 2006, chapter 8) of how they relate to each other, and which can be expected to give rise to the best algorithms.

In this paper we provide a unifying view of sparse approximations for GP regression. Our approach is simple, but powerful: for each algorithm we analyze the posterior, and compute the effective prior which it is using. Thus, we reinterpret the algorithms as “exact inference with an approximated prior”, rather than the existing (ubiquitous) interpretationapproximate inference with the exact prior”. This approach has the advantage of directly expressing the approximations in terms of prior assumptions about the function, which makes the consequences of the approximations much easier to understand. While our view of the approximations is not the only one possible, it has the advantage of putting all existing probabilistic sparse approximations under one umbrella, thus enabling direct comparison and revealing the relation between them.

In Section 1 we briefly introduce GP models for regression. In Section 2 we present our unifying framework and write out the key equations in preparation for the unifying analysis of sparse algorithms in Sections 4-7. The relation of transduction and augmentation to our sparse framework is covered in Section 8. All our approximations are written in terms of a new set of inducing variables. The choice of these variables is itself a challenging problem, and is discussed in Section 9. We comment on a few [[special approximations outside our general scheme in Section 10 and conclusions are drawn at the end.

### 1. Gaussian Processes for Regression

Probabilistic regression is usually formulated as follows: given a training set $D = { (x_i, y_i), i = 1, ..., n}$ of $n$ pairs of (vectorial) inputs xi and noisy (real, scalar) outputs $y_i$, compute the predictive distribution of the function values $f()$ (or noisy y_) at test locations x_.

In the simplest case (which we deal with here) we assume that the noise is additive, independent and Gaussian, such that the relationship between the (latent) function f (x) and the observed noisy targets y are given by yi = f (xi) +ei, where ei _ N (0, s2n oise), (1) where s2 noise is the variance of the noise.

Definition 1
A Gaussian process (GP) is a collection of random variables, any finite number of which have consistent1 joint Gaussian distributions.

Gaussian process (GP) regression is a Bayesian approach which assumes a GP prior[1] over functions, i.e. assumes a priori that function values behave according to : $p (\mathbf{f}|x_1, x_2,..., x_n) = \mathcal{N} (0, K), (2)$ where $f = [f_1, f_2,..., f_n]^T$ is a vector of latent function values, $f_i = f(\mathbf{x}_i)$ and K is a covariance matrix, whose entries are given by the covariance function, $K_{ij} = k (x_i, x_j)$. Note that the GP treats the latent function values $f_i$ as random variables, indexed by the corresponding input. In the following, for simplicity we will always neglect the explicit conditioning on the inputs; the GP model and all expressions are always conditional on the corresponding inputs. The GP model is concerned only with the conditional of the outputs given the inputs; we do not model anything about the inputs themselves.

## References

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volumeDate ValuetitletypejournaltitleUrldoinoteyear
2005 AUnifyingViewofSparseApproximatA Unifying View of Sparse Approximate Gaussian Process Regression2005
1. For notational simplicity we exclusively use zero-mean priors.
 Author Joaquin Quiñonero-Candela + and Carl Edward Rasmussen + title A Unifying View of Sparse Approximate Gaussian Process Regression + year 2005 +