Hi Jose,
The KDE implementation does work on multivariate data, and will in general
work for multimodal data as well. There are two caveats to that:
1. In the sklearn implementation, the bandwidth must be the same across
each dimension. If this poses a problem for your data, the data can be
scaled before the fit (Using StandardScaler or something similar).
2. The results will depend strongly on the choice of bandwidth: it's
important to cross-validate to determine the optimal bandwidth, as is done
in
http://scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html
Good luck!
Jake
Jake VanderPlas
Director of Research – Physical Sciences
eScience Institute, University of Washington
http://www.vanderplas.com
On Tue, Oct 21, 2014 at 2:09 AM, José Guilherme Camargo de Souza <
jose.camargo.so...@gmail.com> wrote:
> Hi all,
>
> I would like to ask if the density estimation implementation of scikit
> works with multivariate multimodal data. In the digits example [1] it
> is clear that it supports multivariate datasets and in the guide
> description [2] a 1-D bimodal distribution is used.
>
> Is it possible to use the same implementation on multivariate
> gaussian-shaped data with more than 2 modes? If so, are there any
> shortcomings or useful tips when doing that?
>
> Thanks in advance,
> José
>
> [1]
> http://scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html#example-neighbors-plot-digits-kde-sampling-py
> [2]
> http://scikit-learn.org/stable/modules/density.html#kernel-density-estimation
> José Guilherme
>
>
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