NIPS 2012 is already over. Unfortunately, I could not go due to the lack of travel funding. However, as mentioned a few weeks ago, I participated in one paper which is closely related to my Master’s project with Arthur Gretton and Massi Pontil. *Optimal kernel choice for large-scale two-sample tests*. We recently set up a page for the paper where you can download my Matlab implementation of the paper’s methods. Feel free to play around with that. I am currently finishing implementing most methods into the SHOGUN toolbox. We also have a poster which was presented at NIPS. See below for all links.

**Update**: I have completed the kernel selection framework for SHOGUN, it will be included in the next release. See the base class interface here. See an example to use it: single kernel (link) and combined kernels (link). All methods that are mentioned in the paper are included. I also updated the shogun tutorial (link).

At its core, the paper describes a method for selecting the best kernel for two-sample testing with the linear time MMD. Given a kernelÂ $k$Â and terms

$$h_k((x_{i},y_{i}),((x_{j},y_{j}))=k(x_{i},x_{i})+k(y_{i},y_{i})-k(x_{i},y_{j})-k(x_{j},y_{i}),$$

the linear time MMD is their empirical mean,

$$\hat\eta_k=\frac{1}{m}\sum_{i=1}^{m}h((x_{2i-1},y_{2i-1}),(x_{2i},y_{2i})),$$

which is a linear time estimate for the squared distance of the mean embeddings of the distributions where the $x_i, y_i$ come from. The quantity allows to perform a two-sample test, *i.e.* to tell whether the underlying distributions are different.

Given a finite family of kernels $\mathcal{K}$, we select the optimal kernel via maximising the ratio of the MMD statistic by a linear time estimate of the standard deviation of the terms

$$k_*=\arg \sup_{k\in\mathcal{K}}\frac{ \hat\eta_k}{\hat \sigma_k},$$

where $\hat\sigma_k^2$ is a linear time estimate of the variance $\sigma_k^2=\mathbb{E}[h_k^2] – (\mathbb{E}[h_k])^2$ which can also be computed in linear time and constant space. We give a linear time and constant space empirical estimate of this ratio. We establish consistency of this empirical estimate as

$$Â \left\vertÂ \sup_{k\in\mathcal{K}}\hat\eta_k \hat\sigma_k^{-1} -\sup_{k\in\mathcal{K}}\eta_k\sigma_k^{-1}\right\vert=O_P(m^{-\frac{1}{3}}).$$

In addition, we describe a MKL style generalisation to selecting weights of convex combinations of a finite number of baseline kernels,

$$\mathcal{K}:={k : k=\sum_{u=1}^d\beta_uk_u,\sum_{u=1}^d\beta_u\leq D,\beta_u\geq 0, \forall u\in{1,…,d}},$$

via solving the quadratic program

$$\min_\beta { \beta^T\hat{Q}\beta : \beta^T \hat{\eta}=1, \beta\succeq 0},$$

where $\hat{Q}$ is the positive definite empirical covariance matrix of the $h$ terms of all pairs of kernels.

We then describe three experiments to illustrate

- That our criterion outperforms existing methods on synthetic and real-life datasets which correspond to hard two-sample problems
- Why multiple kernels can be an advantage in two-sample testing

See also the description of my Master’s project (link) for details on the experiments.

Download paper

Download poster

Download Matlab code (under the GPLv3)

Supplementary page