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<li><a class="reference internal" href="#">Decomposition</a></li>
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<section id="decomposition">
<span id="decomposition-examples"></span><span id="sphx-glr-auto-examples-decomposition"></span><h1>Decomposition<a class="headerlink" href="#decomposition" title="Permalink to this heading">¶</a></h1>
<p>Examples concerning the <a class="reference internal" href="../../modules/classes.html#module-sklearn.decomposition" title="sklearn.decomposition"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.decomposition</span></code></a> module.</p>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="A plot that compares the various Beta-divergence loss functions supported by the Multiplicative..."><img alt="Beta-divergence loss functions" src="../../_images/sphx_glr_plot_beta_divergence_thumb.png" />
<p><a class="reference internal" href="plot_beta_divergence.html#sphx-glr-auto-examples-decomposition-plot-beta-divergence-py"><span class="std std-ref">Beta-divergence loss functions</span></a></p>
<div class="sphx-glr-thumbnail-title">Beta-divergence loss functions</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example of estimating sources from noisy data."><img alt="Blind source separation using FastICA" src="../../_images/sphx_glr_plot_ica_blind_source_separation_thumb.png" />
<p><a class="reference internal" href="plot_ica_blind_source_separation.html#sphx-glr-auto-examples-decomposition-plot-ica-blind-source-separation-py"><span class="std std-ref">Blind source separation using FastICA</span></a></p>
<div class="sphx-glr-thumbnail-title">Blind source separation using FastICA</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 a..."><img alt="Comparison of LDA and PCA 2D projection of Iris dataset" src="../../_images/sphx_glr_plot_pca_vs_lda_thumb.png" />
<p><a class="reference internal" href="plot_pca_vs_lda.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-lda-py"><span class="std std-ref">Comparison of LDA and PCA 2D projection of Iris dataset</span></a></p>
<div class="sphx-glr-thumbnail-title">Comparison of LDA and PCA 2D projection of Iris dataset</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example applies to olivetti_faces_dataset different unsupervised matrix decomposition (dim..."><img alt="Faces dataset decompositions" src="../../_images/sphx_glr_plot_faces_decomposition_thumb.png" />
<p><a class="reference internal" href="plot_faces_decomposition.html#sphx-glr-auto-examples-decomposition-plot-faces-decomposition-py"><span class="std std-ref">Faces dataset decompositions</span></a></p>
<div class="sphx-glr-thumbnail-title">Faces dataset decompositions</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Investigating the Iris dataset, we see that sepal length, petal length and petal width are high..."><img alt="Factor Analysis (with rotation) to visualize patterns" src="../../_images/sphx_glr_plot_varimax_fa_thumb.png" />
<p><a class="reference internal" href="plot_varimax_fa.html#sphx-glr-auto-examples-decomposition-plot-varimax-fa-py"><span class="std std-ref">Factor Analysis (with rotation) to visualize patterns</span></a></p>
<div class="sphx-glr-thumbnail-title">Factor Analysis (with rotation) to visualize patterns</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates visually in the feature space a comparison by results using two differ..."><img alt="FastICA on 2D point clouds" src="../../_images/sphx_glr_plot_ica_vs_pca_thumb.png" />
<p><a class="reference internal" href="plot_ica_vs_pca.html#sphx-glr-auto-examples-decomposition-plot-ica-vs-pca-py"><span class="std std-ref">FastICA on 2D point clouds</span></a></p>
<div class="sphx-glr-thumbnail-title">FastICA on 2D point clouds</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="An example comparing the effect of reconstructing noisy fragments of a raccoon face image using..."><img alt="Image denoising using dictionary learning" src="../../_images/sphx_glr_plot_image_denoising_thumb.png" />
<p><a class="reference internal" href="plot_image_denoising.html#sphx-glr-auto-examples-decomposition-plot-image-denoising-py"><span class="std std-ref">Image denoising using dictionary learning</span></a></p>
<div class="sphx-glr-thumbnail-title">Image denoising using dictionary learning</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Incremental principal component analysis (IPCA) is typically used as a replacement for principa..."><img alt="Incremental PCA" src="../../_images/sphx_glr_plot_incremental_pca_thumb.png" />
<p><a class="reference internal" href="plot_incremental_pca.html#sphx-glr-auto-examples-decomposition-plot-incremental-pca-py"><span class="std std-ref">Incremental PCA</span></a></p>
<div class="sphx-glr-thumbnail-title">Incremental PCA</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the difference between the Principal Components Analysis (:class:`~sklearn.d..."><img alt="Kernel PCA" src="../../_images/sphx_glr_plot_kernel_pca_thumb.png" />
<p><a class="reference internal" href="plot_kernel_pca.html#sphx-glr-auto-examples-decomposition-plot-kernel-pca-py"><span class="std std-ref">Kernel PCA</span></a></p>
<div class="sphx-glr-thumbnail-title">Kernel PCA</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Probabilistic PCA and Factor Analysis are probabilistic models. The consequence is that the lik..."><img alt="Model selection with Probabilistic PCA and Factor Analysis (FA)" src="../../_images/sphx_glr_plot_pca_vs_fa_model_selection_thumb.png" />
<p><a class="reference internal" href="plot_pca_vs_fa_model_selection.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-fa-model-selection-py"><span class="std std-ref">Model selection with Probabilistic PCA and Factor Analysis (FA)</span></a></p>
<div class="sphx-glr-thumbnail-title">Model selection with Probabilistic PCA and Factor Analysis (FA)</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Principal Component Analysis applied to the Iris dataset."><img alt="PCA example with Iris Data-set" src="../../_images/sphx_glr_plot_pca_iris_thumb.png" />
<p><a class="reference internal" href="plot_pca_iris.html#sphx-glr-auto-examples-decomposition-plot-pca-iris-py"><span class="std std-ref">PCA example with Iris Data-set</span></a></p>
<div class="sphx-glr-thumbnail-title">PCA example with Iris Data-set</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="These figures aid in illustrating how a point cloud can be very flat in one direction--which is..."><img alt="Principal components analysis (PCA)" src="../../_images/sphx_glr_plot_pca_3d_thumb.png" />
<p><a class="reference internal" href="plot_pca_3d.html#sphx-glr-auto-examples-decomposition-plot-pca-3d-py"><span class="std std-ref">Principal components analysis (PCA)</span></a></p>
<div class="sphx-glr-thumbnail-title">Principal components analysis (PCA)</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Transform a signal as a sparse combination of Ricker wavelets. This example visually compares d..."><img alt="Sparse coding with a precomputed dictionary" src="../../_images/sphx_glr_plot_sparse_coding_thumb.png" />
<p><a class="reference internal" href="plot_sparse_coding.html#sphx-glr-auto-examples-decomposition-plot-sparse-coding-py"><span class="std std-ref">Sparse coding with a precomputed dictionary</span></a></p>
<div class="sphx-glr-thumbnail-title">Sparse coding with a precomputed dictionary</div>
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