I have downloaded the most recent GPML Matlab code GPML Matlab code and I have read the documentation and ran the regression demo without any problems. However, I am having difficulty understanding how to apply it to a regression problem that I am faced with.

The regression problem is defined as follows:

Let $\mathbf{x}_i \in \mathbb{R}^{20}$ be an input vector and $\mathbf{y}_i \in \mathbb{R}^{25}$ be its corresponding target. The set of $M$ inputs are arranged into a matrix $\mathbf{X} = [\mathbf{x}_1, \dots, \mathbf{x}_M]^\top$ and their corresponding targets are stored in a matrix $\mathbf{Y} = [\mathbf{y}_1 - \mathbf{\bar{y}}, \dots, \mathbf{y}_M-\mathbf{\bar{y}}]^\top$, with $\mathbf{\bar{y}}$ being the mean target value in $\mathbf{Y}$.

I wish to train a GPR model $\mathcal{G} = \lbrace \mathbf{X}, \mathbf{Y}, \theta \rbrace$ using the squared exponential function:

$k(\mathbf{x}_i, \mathbf{x}_j) = \alpha^2 \text{exp} \left( - \frac{1}{2\beta^2}(\mathbf{x}_i - \mathbf{x}_j)^2\right) + \gamma^2\delta_{ij}$,

where $\delta_{ij}$ equals $1$ if $i = j$ and $0$ otherwise. The hyperparameters are $\theta = (\alpha, \beta, \gamma)$ with $\gamma$ being the assumed noise level in the training data and $\beta$ is the length-scale.

To train the model, I need to minimise the negative log marginal likelihood with respect to the hyperparameters:

$-\text{log}\, p(\mathbf{Y} \mid \mathbf{X}, \theta) = \frac{1}{2} \text{tr}(\mathbf{Y}^\top\mathbf{K}^{-1}\mathbf{Y}) + \frac{1}{2}\text{log}\mid\mathbf{K}\mid + \,c,$

where c is a constant and the matrix $\mathbf{K}$ is a function of the hyperparameters (see equation k(xi,xj) = ...).

Based on the demo given on the GPML website, my attempt at implementing this using the GPML Matlab code is below.

covfunc = @covSEiso;
likfunc = @likGauss;
sn = 0.1;
hyp.lik = log(sn);
hyp2.cov = [0;0];
hyp2.lik = log(0.1);
hyp2 = minimize(hyp2, @gp, -100, @infExact, [], covfunc, likfunc, X1, Y1(:, n));
nlml2 = gp(hyp2, @infExact, [], covfunc, likfunc, X1, Y1(:, n));
[m s2] = gp(hyp2, @infExact, [], covfunc, likfunc, X1, Y1(:, n), X2);
Y2r(:, n) = m;

X1 contains the training inputs

X2 contains the test inputs

Y1 contains the training targets

Y2r are the estimates from applying the model

n is the index used to regress each element in the output vector

Given the problem, is this the correct way to train and apply the GPR model? If not, what do I need to change?


2 Answers 2


The GP does a good job for your problem's training data. However, it's not so great for the test data. You've probably already ran something like the following yourself:


for N = 1 : 25
    % normalize
    m = mean(Y1(N,:));
    s = std(Y1(N,:));
    Y1(N,:) = 1/s * (Y1(N,:) - m);
    Y2(N,:) = 1/s * (Y2(N,:) - m);

    covfunc = @covSEiso;
    ell = 2;
    sf = 1;
    hyp.cov = [ log(ell); log(sf)];

    likfunc = @likGauss;
    sn = 1;
    hyp.lik = log(sn);

    hyp = minimize(hyp, @gp, -100, @infExact, [], covfunc, likfunc, X1', Y1(N,:)');
    [m s2] = gp(hyp, @infExact, [], covfunc, likfunc, X1', Y1(N,:)', X1');    
    subplot(2,1,1); hold on;    
    title(['N = ' num2str(N)]);    
    f = [m+2*sqrt(s2); flipdim(m-2*sqrt(s2),1)];
    x = [1:length(m)];
    fill([x'; flipdim(x',1)], f, [7 7 7]/8);
    plot(Y1(N,:)', 'b');
    plot(m, 'r');
    mse_train = mse(Y1(N,:)' - m);

    [m s2] = gp(hyp, @infExact, [], covfunc, likfunc, X1', Y1(N,:)', X2');
    subplot(2,1,2); hold on;
    f = [m+2*sqrt(s2); flipdim(m-2*sqrt(s2),1)];
    x = [1:length(m)];
    fill([x'; flipdim(x',1)], f, [7 7 7]/8);    
    plot(Y2(N,:)', 'b');
    plot(m, 'r');
    mse_test = mse(Y2(N,:)' - m);

    disp(sprintf('N = %d -- train = %5.2f   test = %5.2f', N, mse_train, mse_test));

Tuning the hyperparameters manually and not using the minimize function it is possible to balance the train and test error somewhat, but tuning the method by looking at the test error is not what you're supposed to do. I think what's happening is heavy overfitting to your three subjects that generated the training data. No method will out-of-the-box do a good job here, and how could it? You provide the training data, so the method tries to get as good as possible on the training data without overfitting. And it fact, it doesn't overfit in the classical sense. It doesn't overfit to the data, but it overfits to the three training subjects. E.g., cross-validating with the training set would tell us that there's no overfitting. Still, your test set will be explained poorly.

What you can do is:

  1. Get data from more subjects for training. This way your fourth person will be less likely to look like an "outlier" as it does currently. Also, you have just one sequence of each person, right? Maybe it would help to record the sequence multiple times.

  2. Somehow incorporate prior knowledge about your task that would keep a method from overfitting to specific subjects. In a GP that could be done via the covariance function, but it's probably not that easy to do ...

  3. If I'm not mistaken, the sequences are in fact time-series. Maybe it would make sense to exploit the temporal relations, for instance using recurrent neural networks.

There's most definitely more, but those are the things I can think of right now.

  • $\begingroup$ I am assuming a zero-mean Gaussian process. Since the targets do not have a zero mean I centre them by subtracting their mean. You're right about the redundancy; I have removed those two lines. I don't believe the covariance function is correct given the problem, and the I'm not confident about the intitialisation of the hyperparameters. My doubts come about from the results. The residuals are practically the same as those for ridge regression, and my data is known to be highly nonlinear. $\endgroup$
    – Josh
    Jun 29, 2011 at 9:36
  • $\begingroup$ You're right about the substraction; it shouldn't hurt in any case. I'd normally add that to the covariance function, like covfunc = { 'covSum', { 'covSEiso' } } I don't quite see how this takes care of noisy data now, it seems the toolbox has changed quite a lot since I last used it, will have a closer look at it later. $\endgroup$
    – ahans
    Jun 29, 2011 at 22:02
  • $\begingroup$ What do you know about your problem that makes you think that the covSEiso isn't a reasonable choice? And the ridge regression you use, is that a kernlized one or linear? If you use kernels, it's not that surprising that you get similar results. $\endgroup$
    – ahans
    Jun 29, 2011 at 22:05
  • $\begingroup$ Can you provide sample data of your problem? That would make things a bit easier, perhaps with just one target dimension. $\endgroup$
    – ahans
    Jun 29, 2011 at 22:07
  • 1
    $\begingroup$ @Masood You're right, but when $n>30$ the Student t distribution is very close to the gaussian distribution. Even with n>20, we generally have a good approximation. $\endgroup$
    – chl
    Sep 13, 2011 at 22:23

I think the problem may be one of model mis-specification. If your targets are angles wrapped to +-180 degrees, then the "noise process" for your data may be sufficiently non-Guassian that the Baysian evidence is not a good way to optimise the hyper-parameters. For instance, consider what happens when "noise" causes the signal to wrap-around. In that case it may be wise to either perform model selection by minimising the cross-validation error (there is a public domain implementation of the Nelder-Mead simplex method here if you don't have the optimisation toolbox). The cross-validation estimate of performance is not so sensitive to model mis-specification as it is a direct estimate of test performance, whereas the marginal likelihood of the model is the evidence in suport of the model given that the model assumptions are correct. See the discussion starting on page 123 of Rasmussen and Williams' book.

Another approach would be to re-code the outputs so that a Gaussian noise model is more appropriate. One thing you could do is some form of unsupervised dimensionality reduction, as there are non-linear relationships between your targets (as there are only a limited way in which a body can move), so there will be a lower-dimensional manifold that your targets live on, and it would be better to regress the co-ordinates of that manifold rather than the angles themselves (there may be fewer targets that way as well).

Also some sort of Procrustes analysis might be a good idea to normalise the differences between subjects before training the model.

You may find some of the work done by Neil Lawrence on human pose recovery of interest. I remember seeing a demo of this at a conference a few years ago and was very impressed.

  • $\begingroup$ From my analysis, I have noticed that the discontinuities in the output space cause a number of problems. I have considered using joint locations rather than joint angles to overcome this problem. By dimensionality reduction, did you have a particular method in mind? Unlike image-based approaches, I don't see how the differences in subjects (other than their movement patterns) would effect the training of the model, given that I am using orientations of IMU sensors which are consistently placed and post-processed to be aligned between subjects. $\endgroup$
    – Josh
    Jul 2, 2011 at 16:19
  • $\begingroup$ I have come across Lawrence's paper before. Since only 1 sequence was mentioned in the paper, it seems that some form of k-fold CV was performed. In which case, the problem becomes almost trivial. Same-subject mappings of an activity, particular one that is cyclical, are typically straightforward to obtain decent pose estimates. I have compared same-subject and inter-subject mappings, and the difference is very significant. Unfortunately, research in this area is basically incomparable due to everyone using their own regression framework, mocap data, error metrics, and input/output structures. $\endgroup$
    – Josh
    Jul 2, 2011 at 16:32

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