Question

我知道这个问题已被问过几次，但我无法找到解决问题的方法。

我没有比观察更多的变量，我的矩阵中没有NAN个值。这是我的功能：

function [ind, idx_ran] = fselect(features_f, class_f, dir)

idx = linspace(1,size(features_f, 2), size(features_f, 2));

idx_ran = idx(:,randperm(size(features_f, 2)));

features_t_ran = features_f(:,idx_ran); % randomize colums

len = length(class_f);

r = randi(len, [1, round(len*0.15)]);

x = features_t_ran;
y = class_f;

xtrain = x;
ytrain = y;

xtrain(r,:) = [];
ytrain(r,:) = [];

xtest = x(r,:);
ytest = y(r,:);

f = @(xtrain, ytrain, xtest, ytest)(sum(~strcmp(ytest, classify(xtest, xtrain, ytrain))));
fs = sequentialfs(f, x, y, 'direction', dir);

ind = find(fs < 1);

end

这是我的测试和训练数据。

>> whos xtest
  Name         Size             Bytes  Class     Attributes

  xtest      524x42            176064  double              

>> whos xtrain
  Name           Size              Bytes  Class     Attributes

  xtrain      3008x42            1010688  double              

>> whos ytest
  Name         Size            Bytes  Class    Attributes

  ytest      524x1             32488  cell               

>> whos ytrain
  Name           Size             Bytes  Class    Attributes

  ytrain      3008x1             186496  cell               

>>

这是错误，

Error using crossval>evalFun (line 465)
The function
'@(xtrain,ytrain,xtest,ytest)(sum(~strcmp(ytest,classify(xtest,xtrain,ytrain))))' generated
the following error:
The pooled covariance matrix of TRAINING must be positive definite.

Error in crossval>getFuncVal (line 482)
funResult = evalFun(funorStr,arg(:));

Error in crossval (line 324)
    funResult = getFuncVal(1, nData, cvp, data, funorStr, []);

Error in sequentialfs>callfun (line 485)
    funResult = crossval(fun,x,other_data{:},...

Error in sequentialfs (line 353)
                crit(k) = callfun(fun,x,other_data,cv,mcreps,ParOptions);

Error in fselect (line 26)
fs = sequentialfs(f, x, y, 'direction', dir);

Error in workflow_forward (line 31)
    [ind, idx_ran] = fselect(features_f, class_f, 'forward');

这是昨天的工作。：/

Answer 1

如果检查函数classify，您会发现当程序检查从训练矩阵的QR分解中获得的矩阵R的条件数时会产生错误。换句话说，它对您提供的训练矩阵不满意。它发现这个矩阵是病态的，因此任何解决方案都是不稳定的（该函数执行等效的矩阵求逆，这将导致相当于病态训练矩阵的非常小的除数）。

似乎通过缩小训练集的大小，稳定性降低了。我的建议是尽可能使用更大的训练集。

修改

您可能想知道如何获得比变量更多的观察结果并且仍然存在病态问题。答案是不同的观察结果可以是彼此的线性组合。

培训的汇总协方差矩阵必须是正定的

1 个答案: