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</style><section id="unsupervised-learning">
<span id="id1"></span><h1><span class="section-number">2. </span>Unsupervised learning<a class="headerlink" href="#unsupervised-learning" title="Link to this heading">¶</a></h1>
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<li class="toctree-l2"><a class="reference internal" href="modules/mixture.html#gaussian-mixture">2.1.1. Gaussian Mixture</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/mixture.html#variational-bayesian-gaussian-mixture">2.1.2. Variational Bayesian Gaussian Mixture</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html#optics">2.3.9. OPTICS</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html#birch">2.3.10. BIRCH</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/clustering.html#clustering-performance-evaluation">2.3.11. Clustering performance evaluation</a></li>
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</li>
<li class="toctree-l1"><a class="reference internal" href="modules/biclustering.html">2.4. Biclustering</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/biclustering.html#spectral-co-clustering">2.4.1. Spectral Co-Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/biclustering.html#spectral-biclustering">2.4.2. Spectral Biclustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/biclustering.html#biclustering-evaluation">2.4.3. Biclustering evaluation</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/decomposition.html">2.5. Decomposing signals in components (matrix factorization problems)</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#principal-component-analysis-pca">2.5.1. Principal component analysis (PCA)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#kernel-principal-component-analysis-kpca">2.5.2. Kernel Principal Component Analysis (kPCA)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#truncated-singular-value-decomposition-and-latent-semantic-analysis">2.5.3. Truncated singular value decomposition and latent semantic analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#dictionary-learning">2.5.4. Dictionary Learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#factor-analysis">2.5.5. Factor Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#independent-component-analysis-ica">2.5.6. Independent component analysis (ICA)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#non-negative-matrix-factorization-nmf-or-nnmf">2.5.7. Non-negative matrix factorization (NMF or NNMF)</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#latent-dirichlet-allocation-lda">2.5.8. Latent Dirichlet Allocation (LDA)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/covariance.html">2.6. Covariance estimation</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html#empirical-covariance">2.6.1. Empirical covariance</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html#shrunk-covariance">2.6.2. Shrunk Covariance</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html#sparse-inverse-covariance">2.6.3. Sparse inverse covariance</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html#robust-covariance-estimation">2.6.4. Robust Covariance Estimation</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/outlier_detection.html">2.7. Novelty and Outlier Detection</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html#overview-of-outlier-detection-methods">2.7.1. Overview of outlier detection methods</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html#novelty-detection">2.7.2. Novelty Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html#id1">2.7.3. Outlier Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html#novelty-detection-with-local-outlier-factor">2.7.4. Novelty detection with Local Outlier Factor</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/density.html">2.8. Density Estimation</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/density.html#density-estimation-histograms">2.8.1. Density Estimation: Histograms</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/density.html#kernel-density-estimation">2.8.2. Kernel Density Estimation</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="modules/neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/neural_networks_unsupervised.html#restricted-boltzmann-machines">2.9.1. Restricted Boltzmann machines</a></li>
</ul>
</li>
</ul>
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