資源簡介
用matlab實現LPP算法,LPP算法在人臉等生物識別中具有廣泛的應用
代碼片段和文件信息
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?=?\le
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