mhjensen
149 lines
5.0 KiB
HTML
149 lines
5.0 KiB
HTML
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<title>Data Analysis and Machine Learning: Dimensionality Reduction</title>
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<!-- navigation toc: --> <li><a href="#___sec0" style="font-size: 80%;">Reducing the number of degrees of freedom, overarching view</a></li>
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<h2 id="___sec0" class="anchor">Reducing the number of degrees of freedom, overarching view </h2>
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<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
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<p>
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Many Machine Learning problems involve thousands or even millions of features for each training
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instance. Not only does this make training extremely slow, it can also make it much harder to find a good
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solution, as we will see. This problem is often referred to as the curse of dimensionality.
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Fortunately, in real-world problems, it is often possible to reduce the number of features considerably,
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turning an intractable problem into a tractable one.
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<p>
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and we will go through three of the most popular dimensionality
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reduction techniques: PCA, Kernel PCA, and LLE.
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<li><a href="._DimRed-bs000.html">1</a></li>
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<li class="active"><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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<li><a href="._DimRed-bs005.html">6</a></li>
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