function??[pdataPCV]?=?pca1（data）
%?PCA1:???Perform?principal?component?analysis?using?covariance.
%
%?Usage:??[pdataPCV]?=?pca1（data）
%
%?Principal?Component?Analysis?（PCA）?is?a?method?of?diagonlizing
%?the?covarince?matrix?of?a?data?set.?In?real?world?applications
%?this?means?that?the?PCA?can?find?the?bases?which?maximize?the
%?variance?and?allow?for?easier?dimesional?reduction.?There?are
%?several?algorithms?for?implementing?this?analysis.?This
%?method?involves?the?explicit?calculation?of?the?covariance.
%
%?Arguments:
%
%?????data?-?matrix?（MxN）?where?M?is?the?number?of?dimensions?of
%????????????the?data?set?and?N?is?the?number?of?data?set?trials.
%
%????????????In?other?words?each?row?is?one?type?of?variable?and
%????????????each?column?is?is?observation?of?the?data?set?（with?M
%????????????variables）
%
%?Outputs:
%
%??pdata?-?the?projected?data.?This?MxN?matrix?is?the?original?data
%??????????set?projected?on?to?the?principal?component?basis.?This
%??????????is?basically

?屬性????????????大小?????日期????時間???名稱
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?????文件???????3826??2002-08-26?10:28??pca\pca1.m

?????文件???????2570??2003-03-20?14:02??pca\pca2.m

?????目錄??????????0??2003-03-20?14:02??pca

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