mhjensen
145 lines
5.3 KiB
HTML
145 lines
5.3 KiB
HTML
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<meta name="description" content="Data Analysis and Machine Learning: Dimensionality Reduction">
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<title>Data Analysis and Machine Learning: Dimensionality Reduction</title>
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<!-- tocinfo
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{'highest level': 2,
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'sections': [('Reducing the number of degrees of freedom, overarching view',
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2,
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None,
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'___sec0'),
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('Principal Component Analysis', 2, None, '___sec1'),
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('Kernel PCA', 2, None, '___sec2'),
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('LLE', 2, None, '___sec3'),
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('Other techniques', 2, None, '___sec4')]}
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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>
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<ul class="dropdown-menu">
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<!-- navigation toc: --> <li><a href="._DimRed-bs001.html#___sec0" style="font-size: 80%;">Reducing the number of degrees of freedom, overarching view</a></li>
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<!-- navigation toc: --> <li><a href="._DimRed-bs002.html#___sec1" style="font-size: 80%;">Principal Component Analysis</a></li>
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<!-- navigation toc: --> <li><a href="._DimRed-bs003.html#___sec2" style="font-size: 80%;">Kernel PCA</a></li>
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<!-- navigation toc: --> <li><a href="._DimRed-bs004.html#___sec3" style="font-size: 80%;">LLE</a></li>
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<!-- navigation toc: --> <li><a href="#___sec4" style="font-size: 80%;">Other techniques</a></li>
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</ul>
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</li>
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</ul>
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</div> <!-- end of navigation bar -->
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<div class="container">
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<p> </p><p> </p><p> </p> <!-- add vertical space -->
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<a name="part0005"></a>
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<!-- !split -->
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<h2 id="___sec4" class="anchor">Other techniques </h2>
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<p>
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There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn.
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Here are some of the most popular:
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Multidimensional Scaling (MDS) reduces dimensionality while trying to preserve the distances
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between the instances (see Figure 8-13).
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Isomap creates a graph by connecting each instance to its nearest neighbors, then reduces
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dimensionality while trying to preserve the geodesic distances9 between the instances.
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t-Distributed Stochastic Neighbor Embedding (t-SNE) reduces dimensionality while trying to keep
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similar instances close and dissimilar instances apart. It is mostly used for visualization, in
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particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST
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images in 2D).
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Linear Discriminant Analysis (LDA) is actually a classification algorithm, but during training it
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learns the most discriminative axes between the classes, and these axes can then be used to define a
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hyperplane onto which to project the data. The benefit is that the projection will keep classes as far
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apart as possible, so LDA is a good technique to reduce dimensionality before running another
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classification algorithm such as an SVM classifier
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<p>
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<li><a href="._DimRed-bs004.html">«</a></li>
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<li><a href="._DimRed-bs000.html">1</a></li>
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<li><a href="._DimRed-bs001.html">2</a></li>
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<li><a href="._DimRed-bs002.html">3</a></li>
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<li><a href="._DimRed-bs003.html">4</a></li>
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<li><a href="._DimRed-bs004.html">5</a></li>
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