资源简介
用matlab实现LPP算法,LPP算法在人脸等生物识别中具有广泛的应用
代码片段和文件信息
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
- 上一篇:ARMA法模态参数识别程序
- 下一篇:A*算法路径规划的matlab核心代码
评论
共有 条评论