https://gilgi.org/enContents © 2020 <a href="mailto:site@gilgi.org">gilgi</a> Sat, 23 May 2020 20:33:03 GMTNikola (getnikola.com)http://blogs.law.harvard.edu/tech/rsshttps://gilgi.org/blog/eigenvector/gilgi<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> </div><div class="inner_cell"> <div class="text_cell_render border-box-sizing rendered_html"> <p>Two concepts that are easy to confuse are <a href="https://en.wikipedia.org/wiki/Eigenvalues_and_eigenvectors">eigenvectors</a> and <a href="https://en.wikipedia.org/wiki/Principal_component_analysis">principle components</a>. When the matrix in question is symmetric, there is a relationship between the first eigenvector and the projection of the data onto its first principle component. In this post, we'll use <a href="https://en.wikipedia.org/wiki/Diagonalizable_matrix#Diagonalization">diagonalization</a> and <a href="https://en.wikipedia.org/wiki/Singular_value_decomposition">singular value decomposition</a> to try to shed some light on this.</p> <p><a href="https://colab.research.google.com/github/gilgi/gilgi.github.com/blob/src/posts/eigenvector.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></p> <p><a href="https://gilgi.org/blog/eigenvector/">Read more…</a> (4 min remaining to read)</p></div></div></div>linear algebranotebookPythonhttps://gilgi.org/blog/eigenvector/Sun, 13 Oct 2019 04:00:00 GMT