dct.zip

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  • 2012-04-23 06:06
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dct code for compression
dct.zip
  • dct
  • PCA_in_MATLAB.pdf
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  • dct all.m
    693B
内容介绍
<html xmlns="http://www.w3.org/1999/xhtml"> <head> <meta charset="utf-8"> <meta name="generator" content="pdf2htmlEX"> <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"> <link rel="stylesheet" href="https://static.pudn.com/base/css/base.min.css"> <link rel="stylesheet" href="https://static.pudn.com/base/css/fancy.min.css"> <link rel="stylesheet" href="https://static.pudn.com/prod/directory_preview_static/626cfa4d1fd0a57952812bf8/raw.css"> <script src="https://static.pudn.com/base/js/compatibility.min.js"></script> <script src="https://static.pudn.com/base/js/pdf2htmlEX.min.js"></script> <script> try{ pdf2htmlEX.defaultViewer = new pdf2htmlEX.Viewer({}); }catch(e){} </script> <title></title> </head> <body> <div id="sidebar" style="display: none"> <div id="outline"> </div> </div> <div id="pf1" class="pf w0 h0" data-page-no="1"><div class="pc pc1 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="https://static.pudn.com/prod/directory_preview_static/626cfa4d1fd0a57952812bf8/bg1.jpg"><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">MATLAB CODE FOR PCA </div><div class="t m0 x2 h3 y2 ff1 fs1 fc0 sc1 ls1 ws1">by Jonathon Shlens (Salk Institute for Bi<span class="_ _0"></span>ological Sciences) </div><div class="t m0 x2 h3 y3 ff1 fs1 fc0 sc1 ls2 ws2">http://www.snl.salk.edu/~shlens/pub/notes/pca.pdf </div><div class="t m0 x2 h3 y4 ff1 fs1 fc0 sc1 ls3 ws3"> </div><div class="t m0 x2 h3 y5 ff1 fs1 fc0 sc1 ls1 ws1">This first version follows Secti<span class="_ _0"></span>on 5 (of above tutorial) by examining the covari<span class="_ _0"></span>ance of the data </div><div class="t m0 x2 h3 y6 ff1 fs1 fc0 sc1 ls1 ws3">set. </div><div class="t m0 x2 h3 y7 ff1 fs1 fc0 sc1 ls3 ws3"> </div><div class="t m0 x2 h3 y8 ff1 fs1 fc0 sc1 ls4 ws4">function [signals,PC,V] = pca1(data) </div><div class="t m0 x2 h3 y9 ff1 fs1 fc0 sc1 ls5 ws5">% PCA1: Perform PCA using covariance. </div><div class="t m0 x2 h3 ya ff1 fs1 fc0 sc1 ls2 ws2">% data - MxN matrix of input data </div><div class="t m0 x2 h3 yb ff1 fs1 fc0 sc1 ls6 ws2">% (M dimensions, N trials) </div><div class="t m0 x2 h3 yc ff1 fs1 fc0 sc1 ls7 ws6">% signals - MxN matrix of projected data </div><div class="t m0 x2 h3 yd ff1 fs1 fc0 sc1 ls7 ws6">% PC - each column is a PC </div><div class="t m0 x2 h3 ye ff1 fs1 fc0 sc1 ls8 ws7">% V - Mx1 matrix of variances </div><div class="t m0 x2 h3 yf ff1 fs1 fc0 sc1 ls9 ws8">[M,N] = size(data); </div><div class="t m0 x2 h3 y10 ff1 fs1 fc0 sc1 ls8 ws7">% subtract off the mean for each dimensi<span class="_ _0"></span>on </div><div class="t m0 x2 h3 y11 ff1 fs1 fc0 sc1 lsa ws9">mn = mean(data,2); </div><div class="t m0 x2 h3 y12 ff1 fs1 fc0 sc1 ls9 ws8">data = data - repmat(mn,1,N); </div><div class="t m0 x2 h3 y13 ff1 fs1 fc0 sc1 ls7 ws6">% calculate the covariance matrix </div><div class="t m0 x2 h3 y14 ff1 fs1 fc0 sc1 ls2 ws2">covariance = 1 / (N-1) * data * data&#8217;; </div><div class="t m0 x2 h3 y15 ff1 fs1 fc0 sc1 lsb wsa">% find the eigenvectors and eigenval<span class="_ _0"></span>ues </div><div class="t m0 x2 h3 y16 ff1 fs1 fc0 sc1 lsb wsa">[PC, V] = eig(covariance); </div><div class="t m0 x2 h3 y17 ff1 fs1 fc0 sc1 lsc wsb">% extract diagonal of matrix as vect<span class="_ _0"></span>or </div><div class="t m0 x2 h3 y18 ff1 fs1 fc0 sc1 lsd wsc">V = diag(V); </div><div class="t m0 x2 h3 y19 ff1 fs1 fc0 sc1 ls7 ws6">% sort the variances in decreasing order </div><div class="t m0 x2 h3 y1a ff1 fs1 fc0 sc1 lse wsd">[junk, rindices] = sort(-1*V); </div><div class="t m0 x2 h3 y1b ff1 fs1 fc0 sc1 lsf wse">V = V(rindices); </div></div><div class="pi" data-data='{"ctm":[1.568627,0.000000,0.000000,1.568627,0.000000,0.000000]}'></div></div> </body> </html>
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