unsupervised_reduction.rst

Unsupervised dimensionality reduction

If your number of features is high, it may be useful to reduce it with an unsupervised step prior to supervised steps. Many of the :ref:`unsupervised-learning` methods implement a transform method that can be used to reduce the dimensionality. Below we discuss two specific example of this pattern that are heavily used.

Pipelining

The unsupervised data reduction and the supervised estimator can be chained in one step. See :ref:`pipeline`.

.. currentmodule:: sklearn

PCA: principal component analysis

:class:`decomposition.PCA` looks for a combination of features that capture well the variance of the original features. See :ref:`decompositions`.

Examples

• :ref:`sphx_glr_auto_examples_applications_plot_face_recognition.py`

Random projections

The module: :mod:`random_projection` provides several tools for data reduction by random projections. See the relevant section of the documentation: :ref:`random_projection`.

Examples

• :ref:`sphx_glr_auto_examples_miscellaneous_plot_johnson_lindenstrauss_bound.py`

Feature agglomeration

:class:`cluster.FeatureAgglomeration` applies :ref:`hierarchical_clustering` to group together features that behave similarly.

Examples

• :ref:`sphx_glr_auto_examples_cluster_plot_feature_agglomeration_vs_univariate_selection.py`
• :ref:`sphx_glr_auto_examples_cluster_plot_digits_agglomeration.py`

Feature scaling

Note that if features have very different scaling or statistical properties, :class:`cluster.FeatureAgglomeration` may not be able to capture the links between related features. Using a :class:`preprocessing.StandardScaler` can be useful in these settings.