SMMC 解决流形聚类问题 好用
function [cluster_labels,ppca_label,mse,time_mppca,time_smmc,time_sc,W] = smmc(X,nClusts,ppca_dim,ncentres,knn,power)
%%%% spectral multi-manifold clustering (smmc) algorithm
%%%% Input
% X D by N data matrix D*N的矩阵
% nClusts number of clusters 聚类的数目
% ppca_dim dimension of principal component subspace in PPCA PPCA中主元子空间维数
% ncentres number of centres in the mixture model 混合模型中的中心数
% knn number of nearest neighbors 最近邻数
% power power of affinity 权重
%%%% Output
% cluster_labels the label of each point using smmc
% ppca_label the mixture label of each point in PPCA
% mse average L2 error using the mixture model
% time_mppca running time of MPPCA
% time_smmc running time of smmc
% time_sc running time of spectral clustering on the affinity matrix
% W affinity matrix used in spectral clustering
% Begin copyright notice
%
% May,2010
%
% Written by Yong Wang (yongwang82@gmail.com)
%
% This code is provided as is, with no guarantees except that
% bugs are almost surely present.
%
% Comments and bug reports are welcome.
%
% You are free to modify, extend or distribute this code, as long
% as this copyright notice is included whole and unchanged.
% X =evalin('base','data');
% X=X';
% ppca_dim=2;
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