.. _data_reduction:

===================================== 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 examples of this pattern that are heavily used.

.. topic:: 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.

.. rubric:: Examples

• :ref:sphx_glr_auto_examples_applications_plot_face_recognition.py

Random projections

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

.. rubric:: 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.

.. rubric:: Examples

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

.. topic:: 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.