function?[eigvector?eigvalue?Y]?=?LPP（X?W?options）
%?LPP:?Locality?Preserving?Projections
%
%???????[eigvector?eigvalue]?=?LPP（X?W?options）
%?
%?????????????Input:
%???????????????X???????-?Data?matrix.?Each?row?vector?of?fea?is?a?data?point.
%???????????????W???????-?Affinity?matrix.?You?can?either?call?“constructW“
%?????????????????????????to?construct?the?W?or?construct?it?by?yourself.
%???????????????options?-?Struct?value?in?Matlab.?The?fields?in?options
%?????????????????????????that?can?be?set:
%????????????????????????????ReducedDim???-??The?dimensionality?of?the
%????????????????????????????????????????????reduced?subspace.?If?0
%????????????????????????????????????????????all?the?dimensions?will?be
%????????????????????????????????????????????kept.?Default?is?0.
%????????????????????????????PCARatio?????-??The?percentage?of?principal
%????????????????????????????????????????????component?kept?in?the?PCA
%????????????????????????????????????????????step.?The?percentage?is
%????????????????????????????????????????????calculated?based?on?the
%????????????????????????????????????????????eigenvalue.?Default?is?1
%????????????????????????????????????????????（100%?all?the?non-zero
%????????????????????????????????????????????eigenvalues?will?be?kept.
%?????????????Output:
%???????????????eigvector?-?Each?column?is?an?embedding?function?for?a?new
%???????????????????????????data?point?（row?vector）?x??y?=?x*eigvector
%???????????????????????????will?be?the?embedding?result?of?x.
%???????????????eigvalue??-?The?eigvalue?of?LPP?eigen-problem.?sorted?from
%???????????????????????????smallest?to?largest.?
%?
%?
%???????[eigvector?eigvalue?Y]?=?LPP（X?W?options）???
%???????????????
%???????????????Y:??The?embedding?results?Each?row?vector?is?a?data?point.
%???????????????????Y?=?X*eigvector
%
%
%????Examples:
%
%???????fea?=?rand（5070）;
%???????options?=?[];
%???????options.Metric?=?‘Euclidean‘;
%???????options.NeighborMode?=?‘KNN‘;
%???????options.k?=?5;
%???????options.WeightMode?=?‘HeatKernel‘;
%???????options.t?=?1;
%???????W?=?constructW（feaoptions）;
%???????options.PCARatio?=?0.99
%???????[eigvector?eigvalue?Y]?=?LPP（fea?W?options）;
%???????
%???????
%???????fea?=?rand（5070）;
%???????gnd?=?[ones（101）;ones（151）*2;ones（101）*3;ones（151）*4];
%???????options?=?[];
%???????options.Metric?=?‘Euclidean‘;
%???????options.NeighborMode?=?‘Supervised‘;
%???????options.gnd?=?gnd;
%???????options.bLDA?=?1;
%???????W?=?constructW（feaoptions）;??????
%???????options.PCARatio?=?1;
%???????[eigvector?eigvalue?Y]?=?LPP（fea?W?options）;
%?
%?
%?Note:?After?applying?some?simple?algebra?the?smallest?eigenvalue?problem:
%????X^T*L*X?=?\lemda?X^T*D*X
%??????is?equivalent?to?the?largest?eigenvalue?problem:
%????X^T*W*X?=?\beta?X^T*D*X
%??where?L=D-W;??\lemda=?1?-?\beta.
%?Thus?the?smallest?eigenvalue?problem?can?be?transformed?to?a?largest?
%?eigenvalue?problem.?Such?tr

?屬性????????????大小?????日期????時間???名稱
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?????文件???????5487??2014-07-14?16:25??LPP.m

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?????????????????5487????????????????????1