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- Published on Wednesday, 26 December 2012 00:15

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 kernel \(k\) 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

\[ \left| \sup_{k\in\mathcal{K}}\hat\eta_k \hat\sigma_k^{-1} -\sup_{k\in\mathcal{K}}\eta_k\sigma_k^{-1}\right|=O_P(m^{-\frac{1}{3}}).\]

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

\[\mathcal{K}:=\{k : k=\sum_{u=1}^d\beta_uk_u,\sum_{u=1}^d\beta_u\leq D,\beta_u\geq0, \forall u\in\{1,...,d\}\},\]

via solving the quadratic program

\[\min \{ \beta^T\hat{Q}\beta : \beta^T \hat{\eta}=1, \beta\succeq0\},\]

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