Problem 2049. Six Steps to PCA - Step 2: Covariance

Created by Neil Caithness

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Previous problems in this series<ul><li><a href = "http://www.mathworks.co.uk/matlabcentral/cody/problems/2043-six-steps-to-pca-step-1-centre-and-standardize">Step 1: Centre and Standardize</a></li></ul>Step 2: CovarianceFor the second step we want the covariance matrix of the centred and standardized data that we calculated in Step 1. We could conveniently use Matlab's <tt>cov</tt> function, but this repeats much of what we've already done, for instance centering by removing the mean. It also has to deal with a wider range of possible inputs, whereas we have a very well specified starting point.TaskBuild on the code produced for Step 1 by writing a function to calculate the covariance matrix of the centred and standardized data matrix.Add the following field to those already produced by your <tt>zscore</tt> function.<ul><li><tt>Cov</tt>: a square matrix of covariances</li></ul>Tips<ul><li>It's not as complicated as it sounds. Write one new line of code.</li><li>Don't just call Matlab's own <tt>cov</tt> function.</li><li>Keep to the structure of the code template with <tt>zscore</tt> as a local function.</li></ul>

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Last Solution submitted on Sep 02, 2020

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Jean-Marie Sainthillier on 10 Feb 2014

Where are the other steps Neil ?

Rafael S.T. Vieira on 2 Sep 2020 at 19:34

The problem wants the covariance matrix of the z-score obtained at the previous step if someone else found the problem description unclear.

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Solution 372377

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Neil Caithness on 19 Dec 2013

Now what was so hard about that? :-)
Next I need to work on this pernicious 'ans' rubbish.

J.R.! Menzinger on 19 Dec 2013

HAHAHA I had some fun here, sorry about that... ;-)

but also I forgot the minus one. :-/ So the test-case was wrong. Now I found my mistake.

Solution 372370

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J.R.! Menzinger on 19 Dec 2013

:-P

Solution 371678

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1 Comment

J.R.! Menzinger on 20 Dec 2013

I had the same idea, but I was occupied by bypassing the covariance function :-D

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Problem 2049. Six Steps to PCA - Step 2: Covariance

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