MATLAB实例：PCA降维 |
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MATLAB实例：PCA降维
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MATLAB实例：PCA降维
作者：凯鲁嘎吉 - 博客园 http://www.cnblogs.com/kailugaji/ 1. iris数据 5.1,3.5,1.4,0.2,1 4.9,3.0,1.4,0.2,1 4.7,3.2,1.3,0.2,1 4.6,3.1,1.5,0.2,1 5.0,3.6,1.4,0.2,1 5.4,3.9,1.7,0.4,1 4.6,3.4,1.4,0.3,1 5.0,3.4,1.5,0.2,1 4.4,2.9,1.4,0.2,1 4.9,3.1,1.5,0.1,1 5.4,3.7,1.5,0.2,1 4.8,3.4,1.6,0.2,1 4.8,3.0,1.4,0.1,1 4.3,3.0,1.1,0.1,1 5.8,4.0,1.2,0.2,1 5.7,4.4,1.5,0.4,1 5.4,3.9,1.3,0.4,1 5.1,3.5,1.4,0.3,1 5.7,3.8,1.7,0.3,1 5.1,3.8,1.5,0.3,1 5.4,3.4,1.7,0.2,1 5.1,3.7,1.5,0.4,1 4.6,3.6,1.0,0.2,1 5.1,3.3,1.7,0.5,1 4.8,3.4,1.9,0.2,1 5.0,3.0,1.6,0.2,1 5.0,3.4,1.6,0.4,1 5.2,3.5,1.5,0.2,1 5.2,3.4,1.4,0.2,1 4.7,3.2,1.6,0.2,1 4.8,3.1,1.6,0.2,1 5.4,3.4,1.5,0.4,1 5.2,4.1,1.5,0.1,1 5.5,4.2,1.4,0.2,1 4.9,3.1,1.5,0.1,1 5.0,3.2,1.2,0.2,1 5.5,3.5,1.3,0.2,1 4.9,3.1,1.5,0.1,1 4.4,3.0,1.3,0.2,1 5.1,3.4,1.5,0.2,1 5.0,3.5,1.3,0.3,1 4.5,2.3,1.3,0.3,1 4.4,3.2,1.3,0.2,1 5.0,3.5,1.6,0.6,1 5.1,3.8,1.9,0.4,1 4.8,3.0,1.4,0.3,1 5.1,3.8,1.6,0.2,1 4.6,3.2,1.4,0.2,1 5.3,3.7,1.5,0.2,1 5.0,3.3,1.4,0.2,1 7.0,3.2,4.7,1.4,2 6.4,3.2,4.5,1.5,2 6.9,3.1,4.9,1.5,2 5.5,2.3,4.0,1.3,2 6.5,2.8,4.6,1.5,2 5.7,2.8,4.5,1.3,2 6.3,3.3,4.7,1.6,2 4.9,2.4,3.3,1.0,2 6.6,2.9,4.6,1.3,2 5.2,2.7,3.9,1.4,2 5.0,2.0,3.5,1.0,2 5.9,3.0,4.2,1.5,2 6.0,2.2,4.0,1.0,2 6.1,2.9,4.7,1.4,2 5.6,2.9,3.6,1.3,2 6.7,3.1,4.4,1.4,2 5.6,3.0,4.5,1.5,2 5.8,2.7,4.1,1.0,2 6.2,2.2,4.5,1.5,2 5.6,2.5,3.9,1.1,2 5.9,3.2,4.8,1.8,2 6.1,2.8,4.0,1.3,2 6.3,2.5,4.9,1.5,2 6.1,2.8,4.7,1.2,2 6.4,2.9,4.3,1.3,2 6.6,3.0,4.4,1.4,2 6.8,2.8,4.8,1.4,2 6.7,3.0,5.0,1.7,2 6.0,2.9,4.5,1.5,2 5.7,2.6,3.5,1.0,2 5.5,2.4,3.8,1.1,2 5.5,2.4,3.7,1.0,2 5.8,2.7,3.9,1.2,2 6.0,2.7,5.1,1.6,2 5.4,3.0,4.5,1.5,2 6.0,3.4,4.5,1.6,2 6.7,3.1,4.7,1.5,2 6.3,2.3,4.4,1.3,2 5.6,3.0,4.1,1.3,2 5.5,2.5,4.0,1.3,2 5.5,2.6,4.4,1.2,2 6.1,3.0,4.6,1.4,2 5.8,2.6,4.0,1.2,2 5.0,2.3,3.3,1.0,2 5.6,2.7,4.2,1.3,2 5.7,3.0,4.2,1.2,2 5.7,2.9,4.2,1.3,2 6.2,2.9,4.3,1.3,2 5.1,2.5,3.0,1.1,2 5.7,2.8,4.1,1.3,2 6.3,3.3,6.0,2.5,3 5.8,2.7,5.1,1.9,3 7.1,3.0,5.9,2.1,3 6.3,2.9,5.6,1.8,3 6.5,3.0,5.8,2.2,3 7.6,3.0,6.6,2.1,3 4.9,2.5,4.5,1.7,3 7.3,2.9,6.3,1.8,3 6.7,2.5,5.8,1.8,3 7.2,3.6,6.1,2.5,3 6.5,3.2,5.1,2.0,3 6.4,2.7,5.3,1.9,3 6.8,3.0,5.5,2.1,3 5.7,2.5,5.0,2.0,3 5.8,2.8,5.1,2.4,3 6.4,3.2,5.3,2.3,3 6.5,3.0,5.5,1.8,3 7.7,3.8,6.7,2.2,3 7.7,2.6,6.9,2.3,3 6.0,2.2,5.0,1.5,3 6.9,3.2,5.7,2.3,3 5.6,2.8,4.9,2.0,3 7.7,2.8,6.7,2.0,3 6.3,2.7,4.9,1.8,3 6.7,3.3,5.7,2.1,3 7.2,3.2,6.0,1.8,3 6.2,2.8,4.8,1.8,3 6.1,3.0,4.9,1.8,3 6.4,2.8,5.6,2.1,3 7.2,3.0,5.8,1.6,3 7.4,2.8,6.1,1.9,3 7.9,3.8,6.4,2.0,3 6.4,2.8,5.6,2.2,3 6.3,2.8,5.1,1.5,3 6.1,2.6,5.6,1.4,3 7.7,3.0,6.1,2.3,3 6.3,3.4,5.6,2.4,3 6.4,3.1,5.5,1.8,3 6.0,3.0,4.8,1.8,3 6.9,3.1,5.4,2.1,3 6.7,3.1,5.6,2.4,3 6.9,3.1,5.1,2.3,3 5.8,2.7,5.1,1.9,3 6.8,3.2,5.9,2.3,3 6.7,3.3,5.7,2.5,3 6.7,3.0,5.2,2.3,3 6.3,2.5,5.0,1.9,3 6.5,3.0,5.2,2.0,3 6.2,3.4,5.4,2.3,3 5.9,3.0,5.1,1.8,3 2. MATLAB程序 function [COEFF,SCORE,latent,tsquared,explained,mu,data_PCA]=pca_demo() x=load('iris.data'); [~,d]=size(x); k=d-1; %前k个主成分 x=zscore(x(:,1:d-1)); %归一化数据 [COEFF,SCORE,latent,tsquared,explained,mu]=pca(x); % 1）获取样本数据 X ，样本为行，特征为列。 % 2）对样本数据中心化，得S（S = X的各列减去各列的均值）。 % 3）求 S 的协方差矩阵 C = cov(S) % 4) 对协方差矩阵 C 进行特征分解 [P,Lambda] = eig(C); % 5）结束。 % 1、输入参数 X 是一个 n 行 p 列的矩阵。每行代表一个样本观察数据，每列则代表一个属性，或特征。 % 2、COEFF 就是所需要的特征向量组成的矩阵，是一个 p 行 p 列的矩阵，没列表示一个出成分向量，经常也称为（协方差矩阵的）特征向量。并且是按照对应特征值降序排列的。所以，如果只需要前 k 个主成分向量，可通过：COEFF(:,1:k) 来获得。 % 3、SCORE 表示原数据在各主成分向量上的投影。但注意：是原数据经过中心化后在主成分向量上的投影。即通过：SCORE = x0*COEFF 求得。其中 x0 是中心平移后的 X（注意：是对维度进行中心平移，而非样本。），因此在重建时，就需要加上这个平均值了。 % 4、latent 是一个列向量，表示特征值，并且按降序排列。 % 5、tsquared Hotelling的每个观测值X的T平方统计量 % 6、explained 由每个主成分解释的总方差的百分比 % 7、mu 每个变量X的估计平均值% x= bsxfun(@minus,x,mean(x,1)); data_PCA=x*COEFF(:,1:k); latent1=100*latent/sum(latent);%将latent总和统一为100，便于观察贡献率 pareto(latent1);%调用matla画图 pareto仅绘制累积分布的前95%，因此y中的部分元素并未显示 xlabel('Principal Component'); ylabel('Variance Explained (%)'); % 图中的线表示的累积变量解释程度 print(gcf,'-dpng','Iris PCA.png'); iris_pac=data_PCA(:,1:2) ; save iris_pca iris_pac 3. 结果iris_pca：前两个主成分 -2.25698063306803 0.504015404227653 -2.07945911889541 -0.653216393612590 -2.36004408158421 -0.317413944570283 -2.29650366000389 -0.573446612971233 -2.38080158645275 0.672514410791076 -2.06362347633724 1.51347826673567 -2.43754533573242 0.0743137171331950 -2.22638326740708 0.246787171742162 -2.33413809644009 -1.09148977019584 -2.18136796941948 -0.447131117450110 -2.15626287481026 1.06702095645556 -2.31960685513084 0.158057945820095 -2.21665671559727 -0.706750478104682 -2.63090249246321 -0.935149145374822 -2.18497164997156 1.88366804891533 -2.24394778052703 2.71328133141014 -2.19539570001472 1.50869601039751 -2.18286635818774 0.512587093716441 -1.88775015418968 1.42633236069007 -2.33213619695782 1.15416686250116 -1.90816386828207 0.429027879924458 -2.19728429051438 0.949277150423224 -2.76490709741649 0.487882574439700 -1.81433337754274 0.106394361814184 -2.22077768737273 0.161644638073716 -1.95048968523510 -0.605862870440206 -2.04521166172712 0.265126114804279 -2.16095425532709 0.550173363315497 -2.13315967968331 0.335516397664229 -2.26121491382610 -0.313827252316662 -2.13739396044139 -0.482326258880086 -1.82582143036022 0.443780130732953 -2.59949431958629 1.82237008322707 -2.42981076672382 2.17809479520796 -2.18136796941948 -0.447131117450110 -2.20373717203888 -0.183722323644913 -2.03759040170113 0.682669420156327 -2.18136796941948 -0.447131117450110 -2.42781878392261 -0.879223932713649 -2.16329994558551 0.291749566745466 -2.27889273592867 0.466429134628597 -1.86545776627869 -2.31991965918865 -2.54929404704891 -0.452301129580194 -1.95772074352968 0.495730895348582 -2.12624969840005 1.16752080832811 -2.06842816583668 -0.689607099127106 -2.37330741591874 1.14679073709691 -2.39018434748641 -0.361180775489047 -2.21934619663183 1.02205856145225 -2.19858869176329 0.0321302060908945 1.10030752013391 0.860230593245533 0.730035752246062 0.596636784545418 1.23796221659453 0.612769614333371 0.395980710562889 -1.75229858398514 1.06901265623960 -0.211050862633647 0.383174475987114 -0.589088965722193 0.746215185580377 0.776098608766709 -0.496201068006129 -1.84269556949638 0.923129796737431 0.0302295549588077 0.00495143780650871 -1.02596403732389 -0.124281108093219 -2.64918765259090 0.437265238506424 -0.0586846858581760 0.549792126592992 -1.76666307900171 0.714770518429262 -0.184815166484382 -0.0371339806719297 -0.431350035919633 0.872966018474250 0.508295314415273 0.346844440799832 -0.189985178614466 0.152880381053472 -0.788085297090142 1.21124542423444 -1.62790202112846 0.156417163578196 -1.29875232891050 0.735791135537219 0.401126570248885 0.470792483676532 -0.415217206131680 1.22388807504403 -0.937773165086814 0.627279600231826 -0.415419947028686 0.698133985336190 -0.0632819273014206 0.870620328215835 0.249871517845242 1.25003445866275 -0.0823442389434431 1.35370481019450 0.327722365822153 0.659915359649250 -0.223597000167979 -0.0471236447211597 -1.05368247816741 0.121128417400412 -1.55837168956507 0.0140710866007487 -1.56813894313840 0.235222818975321 -0.773333046281646 1.05316323317206 -0.634774729305402 0.220677797156699 -0.279909968621073 0.430341476713787 0.852281697154445 1.04590946111265 0.520453696157683 1.03241950881290 -1.38781716762055 0.0668436673617666 -0.211910813930204 0.274505447436587 -1.32537578085168 0.271425764670620 -1.11570381243558 0.621089830946741 0.0274506709978046 0.328903506457842 -0.985598883763833 -0.372380114621411 -2.01119457605980 0.281999617970590 -0.851099454545845 0.0887557702224096 -0.174324544331148 0.223607676665854 -0.379214256409087 0.571967341693057 -0.153206717308028 -0.455486948803962 -1.53432438068788 0.251402252309636 -0.593871222060355 1.84150338645482 0.868786147264828 1.14933941416981 -0.698984450845645 2.19898270027627 0.552618780551384 1.43388176486790 -0.0498435417617587 1.86165398830779 0.290220535935809 2.74500070081969 0.785799704159685 0.357177895625210 -1.55488557249365 2.29531637451915 0.408149356863061 1.99505169024551 -0.721448439846371 2.25998344407884 1.91502747107928 1.36134878398531 0.691631011499905 1.59372545693795 -0.426818952656741 1.87796051113409 0.412949339203311 1.24890257443547 -1.16349352357816 1.45917315700813 -0.442664601834978 1.58649439864337 0.674774813132046 1.46636772102851 0.252347085727036 2.42924030093571 2.54822056527013 3.29809226641255 -0.00235343587272177 1.24979406018816 -1.71184899071237 2.03368323142868 0.904369044486726 0.970663302005081 -0.569267277965818 2.88838806680663 0.396463170625287 1.32475563655861 -0.485135293486995 1.69855040646181 1.01076227706927 1.95119099025002 0.999984474306318 1.16799162725452 -0.317831851008113 1.01637609822602 0.0653241212065782 1.78004554289349 -0.192627479858818 1.85855159177699 0.553527164026207 2.42736549094542 0.245830911619345 2.30834922706014 2.61741528404554 1.85415981777379 -0.184055790370030 1.10756129219332 -0.294997832217552 1.19347091639304 -0.814439294423699 2.79159729280499 0.841927657717863 1.57487925633390 1.06889360300461 1.34254676764379 0.420846092290459 0.920349720485088 0.0191661621187343 1.84736314547313 0.670177571688802 2.00942543830962 0.608358978317639 1.89676252747561 0.683734258412757 1.14933941416981 -0.698984450845645 2.03648602144585 0.861797777652503 1.99500750598298 1.04504903502442 1.86427657131500 0.381543630923962 1.55328823048458 -0.902290843047121 1.51576710303099 0.265903772450991 1.37179554779330 1.01296839034343 0.956095566421630 -0.0222095406309480累计贡献率
可见：前两个主成分已经占了95%的贡献程度。这两个主成分可以近似表示整个数据。 4. pca_data.m其中normlization.m见MATLAB实例：聚类初始化方法与数据归一化方法 function data=pca_data(data, choose) % PCA降维，保留90%的特征信息 data = normlization(data, choose); %归一化 score = 0.90; %保留90%的特征信息 [num,dim] = size(data); xbar = mean(data,1); means = bsxfun(@minus, data, xbar); cov = means'*means/num; [V,D] = eig(cov); eigval = diag(D); [~,idx] = sort(eigval,'descend'); eigval = eigval(idx); V = V(idx,:); p = 0; for i=1:dim perc = sum(eigval(1:i))/sum(eigval); if perc > score p = i; break; end end E = V(1:p,:); data= means*E';参考： Junhao Hua. Distributed Variational Bayesian Algorithms. Github, 2017. MATLAB实例：PCA（主成成分分析）详解 |
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