.. _example_plot_johnson_lindenstrauss_bound.py: ===================================================================== The Johnson-Lindenstrauss bound for embedding with random projections ===================================================================== The `Johnson-Lindenstrauss lemma`_ states that any high dimensional dataset can be randomly projected into a lower dimensional Euclidean space while controlling the distortion in the pairwise distances. .. _`Johnson-Lindenstrauss lemma`: http://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma Theoretical bounds ================== The distortion introduced by a random projection `p` is asserted by the fact that `p` is defining an eps-embedding with good probability as defined by: (1 - eps) ||u - v||^2 < ||p(u) - p(v)||^2 < (1 + eps) ||u - v||^2 Where u and v are any rows taken from a dataset of shape [n_samples, n_features] and p is a projection by a random Gaussian N(0, 1) matrix with shape [n_components, n_features] (or a sparse Achlioptas matrix). The minimum number of components to guarantees the eps-embedding is given by: n_components >= 4 log(n_samples) / (eps^2 / 2 - eps^3 / 3) The first plot shows that with an increasing number of samples ``n_samples``, the minimal number of dimensions ``n_components`` increased logarithmically in order to guarantee an ``eps``-embedding. The second plot shows that an increase of the admissible distortion ``eps`` allows to reduce drastically the minimal number of dimensions ``n_components`` for a given number of samples ``n_samples`` Empirical validation ==================== We validate the above bounds on the the digits dataset or on the 20 newsgroups text document (TF-IDF word frequencies) dataset: - for the digits dataset, some 8x8 gray level pixels data for 500 handwritten digits pictures are randomly projected to spaces for various larger number of dimensions ``n_components``. - for the 20 newsgroups dataset some 500 documents with 100k features in total are projected using a sparse random matrix to smaller euclidean spaces with various values for the target number of dimensions ``n_components``. The default dataset is the digits dataset. To run the example on the twenty newsgroups dataset, pass the --twenty-newsgroups command line argument to this script. For each value of ``n_components``, we plot: - 2D distribution of sample pairs with pairwise distances in original and projected spaces as x and y axis respectively. - 1D histogram of the ratio of those distances (projected / original). We can see that for low values of ``n_components`` the distribution is wide with many distorted pairs and a skewed distribution (due to the hard limit of zero ratio on the left as distances are always positives) while for larger values of n_components the distortion is controlled and the distances are well preserved by the random projection. Remarks ======= According to the JL lemma, projecting 500 samples without too much distortion will require at least several thousands dimensions, irrespective of the number of features of the original dataset. Hence using random projections on the digits dataset which only has 64 features in the input space does not make sense: it does not allow for dimensionality reduction in this case. On the twenty newsgroups on the other hand the dimensionality can be decreased from 56436 down to 10000 while reasonably preserving pairwise distances. .. rst-class:: horizontal * .. image:: images/plot_johnson_lindenstrauss_bound_001.png :scale: 47 * .. image:: images/plot_johnson_lindenstrauss_bound_002.png :scale: 47 * .. image:: images/plot_johnson_lindenstrauss_bound_003.png :scale: 47 * .. image:: images/plot_johnson_lindenstrauss_bound_004.png :scale: 47 * .. image:: images/plot_johnson_lindenstrauss_bound_005.png :scale: 47 * .. image:: images/plot_johnson_lindenstrauss_bound_006.png :scale: 47 * .. image:: images/plot_johnson_lindenstrauss_bound_007.png :scale: 47 * .. image:: images/plot_johnson_lindenstrauss_bound_008.png :scale: 47 **Script output**:: Embedding 500 samples with dim 64 using various random projections Projected 500 samples from 64 to 300 in 0.010s Random matrix with size: 0.029MB Mean distances rate: 0.98 (0.08) Projected 500 samples from 64 to 1000 in 0.025s Random matrix with size: 0.096MB Mean distances rate: 1.00 (0.05) Projected 500 samples from 64 to 10000 in 0.248s Random matrix with size: 0.961MB Mean distances rate: 1.01 (0.01) **Python source code:** :download:`plot_johnson_lindenstrauss_bound.py ` .. literalinclude:: plot_johnson_lindenstrauss_bound.py :lines: 90- **Total running time of the example:** 17.09 seconds ( 0 minutes 17.09 seconds)