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<span id="id1"></span><h1>4. Unsupervised learning<a class="headerlink" href="#unsupervised-learning" title="Permalink to this headline">¶</a></h1>
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<li class="toctree-l1"><a class="reference internal" href="modules/mixture.html">4.1. Gaussian mixture models</a><ul>
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<li class="toctree-l4"><a class="reference internal" href="modules/mixture.html#pros">4.1.1.1.1. Pros</a></li>
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<li class="toctree-l4"><a class="reference internal" href="modules/mixture.html#id4">4.1.3.1.1. Pros</a></li>
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<li class="toctree-l4"><a class="reference internal" href="modules/clustering.html#id20">4.3.7.5.2. Advantages</a></li>
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<li class="toctree-l3"><a class="reference internal" href="modules/decomposition.html#sparse-principal-components-analysis-sparsepca-and-minibatchsparsepca">4.4.1.4. Sparse Principal Components Analysis (SparsePCA and MiniBatchSparsePCA)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/decomposition.html#dictionary-learning">4.4.2. Dictionary Learning</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="modules/covariance.html#basic-shrinkage">4.5.2.1. Basic shrinkage</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/covariance.html#ledoit-wolf-shrinkage">4.5.2.2. Ledoit-Wolf shrinkage</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/covariance.html#oracle-approximating-shrinkage">4.5.2.3. Oracle Approximating Shrinkage</a></li>
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<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html#sparse-inverse-covariance">4.5.3. Sparse inverse covariance</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/covariance.html#robust-covariance-estimation">4.5.4. Robust Covariance Estimation</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/covariance.html#minimum-covariance-determinant">4.5.4.1. Minimum Covariance Determinant</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/outlier_detection.html">4.6. Novelty and Outlier Detection</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html#novelty-detection">4.6.1. Novelty Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/outlier_detection.html#id1">4.6.2. Outlier Detection</a><ul>
<li class="toctree-l3"><a class="reference internal" href="modules/outlier_detection.html#fitting-an-elliptic-envelop">4.6.2.1. Fitting an elliptic envelop</a></li>
<li class="toctree-l3"><a class="reference internal" href="modules/outlier_detection.html#one-class-svm-versus-elliptic-envelop">4.6.2.2. One-class SVM versus elliptic envelop</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/hmm.html">4.7. Hidden Markov Models</a></li>
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