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Notes on Principal Component Analysis

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the diagram below, the ellipse represents the scatter diagram of the<br />

sample points. The first principal comp<strong>on</strong>ent is a line through the<br />

widest part; the sec<strong>on</strong>d comp<strong>on</strong>ent is the line at right angles to the<br />

first principal comp<strong>on</strong>ent. In other words, the first principal comp<strong>on</strong>ent<br />

goes through the fattest part of the “football”, and the sec<strong>on</strong>d<br />

principal comp<strong>on</strong>ent through the next fattest part of the “football”<br />

and orthog<strong>on</strong>al to the first; and so <strong>on</strong>. Or, we take our original frame<br />

of reference and do a rigid transformati<strong>on</strong> around the origin to get a<br />

new set of axes; the origin is given by the sample means (of zero) <strong>on</strong><br />

the two X 1 and X 2 variables. (To make these same geometric points,<br />

we could have used a c<strong>on</strong>stant density c<strong>on</strong>tour for a bivariate normal<br />

pair of random variables, X 1 and X 2 , with zero mean vector.)<br />

sec<strong>on</strong>d comp<strong>on</strong>ent<br />

X 2<br />

first comp<strong>on</strong>ent<br />

X 1<br />

As an example of how to find the placement of the comp<strong>on</strong>ents in<br />

the picture given above, suppose we have the two variables, X 1 and<br />

X 2 , with variance-covariance matrix<br />

Σ =<br />

⎛<br />

⎜<br />

⎝<br />

σ1 2 σ 12<br />

σ 12 σ2<br />

2<br />

⎞<br />

⎟<br />

⎠ .<br />

6

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