http://asymptoticlabs.com/enContents © 2022 <a href="mailto:quidditymaster@gmail.com">Tim Anderton</a> Wed, 31 Aug 2022 21:28:20 GMTNikola (getnikola.com)http://blogs.law.harvard.edu/tech/rsshttp://asymptoticlabs.com/posts/slra_sparse_obs.htmlTim Anderton<div tabindex="-1" id="notebook" class="border-box-sizing"> <div class="container" id="notebook-container"> <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"> <h2 id="Intermezzo:-Sparsely-Observed-Data">Intermezzo: Sparsely Observed Data<a class="anchor-link" href="http://asymptoticlabs.com/posts/slra_sparse_obs.html#Intermezzo:-Sparsely-Observed-Data">¶</a></h2><p>In the post on <a href="http://asymptoticlabs.com/posts/other_use_for_PCA_part2.html">using PCA for data imputation</a> we used a weight for each of our data points. By assigning a weight of 0 to missing data and a weight of 1 to the rest of our data we managed to be able to get a reasonably good approximation to what we would find using PCA on the dataset without any data missing.</p> <p>This is fine when evaluating a dense model for our data matrix is not too much computational overhead. However when our input data are sparsely observed, that is to say most of our data consists of missing values then evaluating the model densely is a tremendous waste of computational resources. </p><p><a href="http://asymptoticlabs.com/posts/slra_sparse_obs.html">Read more…</a> (23 min remaining to read)</p></div></div></div></div></div></body></html> machine learningmathjaxPCAreccommender systemssparsityhttp://asymptoticlabs.com/posts/slra_sparse_obs.htmlThu, 26 Oct 2017 06:00:00 GMT