added dim red material

2018-10-24 08:33:06 +02:00
parent ec7b073679
commit d2efb8dd0b
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+<title>Data Analysis and Machine Learning: Dimensionality Reduction</title>
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+<!-- tocinfo
+{'highest level': 2,
+ 'sections': [('Reducing the number of degrees of freedom, overarching view',
+               2,
+               None,
+               '___sec0'),
+              ('Principal Component Analysis', 2, None, '___sec1'),
+              ('Kernel PCA', 2, None, '___sec2'),
+              ('LLE', 2, None, '___sec3'),
+              ('Other techniques', 2, None, '___sec4')]}
+end of tocinfo -->
+
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+    <a class="navbar-brand" href="DimRed-bs.html">Data Analysis and Machine Learning: Dimensionality Reduction</a>
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+        <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
+        <ul class="dropdown-menu">
+     <!-- navigation toc: --> <li><a href="._DimRed-bs001.html#___sec0" style="font-size: 80%;">Reducing the number of degrees of freedom, overarching view</a></li>
+     <!-- navigation toc: --> <li><a href="._DimRed-bs002.html#___sec1" style="font-size: 80%;">Principal Component Analysis</a></li>
+     <!-- navigation toc: --> <li><a href="._DimRed-bs003.html#___sec2" style="font-size: 80%;">Kernel PCA</a></li>
+     <!-- navigation toc: --> <li><a href="._DimRed-bs004.html#___sec3" style="font-size: 80%;">LLE</a></li>
+     <!-- navigation toc: --> <li><a href="#___sec4" style="font-size: 80%;">Other techniques</a></li>
+
+        </ul>
+      </li>
+    </ul>
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+
+<div class="container">
+
+<p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p> <!-- add vertical space -->
+
+<a name="part0005"></a>
+<!-- !split -->
+
+<h2 id="___sec4" class="anchor">Other techniques </h2>
+
+<p>
+There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn.
+Here are some of the most popular:
+Multidimensional Scaling (MDS) reduces dimensionality while trying to preserve the distances
+between the instances (see Figure 8-13).
+Isomap creates a graph by connecting each instance to its nearest neighbors, then reduces
+dimensionality while trying to preserve the geodesic distances9 between the instances.
+t-Distributed Stochastic Neighbor Embedding (t-SNE) reduces dimensionality while trying to keep
+similar instances close and dissimilar instances apart. It is mostly used for visualization, in
+particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST
+images in 2D).
+Linear Discriminant Analysis (LDA) is actually a classification algorithm, but during training it
+learns the most discriminative axes between the classes, and these axes can then be used to define a
+hyperplane onto which to project the data. The benefit is that the projection will keep classes as far
+apart as possible, so LDA is a good technique to reduce dimensionality before running another
+classification algorithm such as an SVM classifier
+
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+  <li><a href="._DimRed-bs004.html">5</a></li>
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