diff --git a/modules/ml/src/em.cpp b/modules/ml/src/em.cpp
index 860a698c29..130c9b87ab 100644
--- a/modules/ml/src/em.cpp
+++ b/modules/ml/src/em.cpp
@@ -44,7 +44,7 @@
 namespace cv
 {
 
-const double minEigenValue = 1.e-5;
+const double minEigenValue = DBL_MIN;
 
 ///////////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -121,7 +121,7 @@ bool EM::trainM(InputArray samples,
 }
 
     
-int EM::predict(InputArray _sample, OutputArray _probs, double* _logLikelihood) const
+int EM::predict(InputArray _sample, OutputArray _probs, double* logLikelihood) const
 {
     Mat sample = _sample.getMat();
     CV_Assert(isTrained());
@@ -135,16 +135,13 @@ int EM::predict(InputArray _sample, OutputArray _probs, double* _logLikelihood)
     }
 
     int label;
-    double logLikelihood = 0.;
     Mat probs;
     if( _probs.needed() )
     {
         _probs.create(1, nclusters, CV_64FC1);
         probs = _probs.getMat();
     }
-    computeProbabilities(sample, label, !probs.empty() ? &probs : 0, _logLikelihood ? &logLikelihood : 0);
-    if(_logLikelihood)
-        *_logLikelihood = logLikelihood;
+    computeProbabilities(sample, label, !probs.empty() ? &probs : 0, logLikelihood);
 
     return label;
 }
@@ -372,6 +369,7 @@ void EM::computeLogWeightDivDet()
     CV_Assert(!covsEigenValues.empty());
 
     Mat logWeights;
+    cv::max(weights, DBL_MIN, weights);
     log(weights, logWeights);
 
     logWeightDivDet.create(1, nclusters, CV_64FC1);
@@ -504,28 +502,24 @@ void EM::computeProbabilities(const Mat& sample, int& label, Mat* probs, double*
     if(!probs && !logLikelihood)
         return;
 
-    if(probs)
-    {
-        Mat expL_Lmax;
-        exp(L - L.at<double>(label), expL_Lmax);
-        double partSum = 0, // sum_j!=q (exp(L_ij - L_iq))
-               factor;      // 1/(1 + partExpSum)
-        for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
-            if(clusterIndex != label)
-                partSum += expL_Lmax.at<double>(clusterIndex);
-        factor = 1./(1 + partSum);
+    Mat buf, *sampleProbs = probs ? probs : &buf;
+    Mat expL_Lmax;
+    exp(L - L.at<double>(label), expL_Lmax);
+    double partSum = 0, // sum_j!=q (exp(L_ij - L_iq))
+           factor;      // 1/(1 + partExpSum)
+    for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
+        if(clusterIndex != label)
+            partSum += expL_Lmax.at<double>(clusterIndex);
+    factor = 1./(1 + partSum);
 
-        probs->create(1, nclusters, CV_64FC1);
-        expL_Lmax *= factor;
-        expL_Lmax.copyTo(*probs);
-    }
+    sampleProbs->create(1, nclusters, CV_64FC1);
+    expL_Lmax *= factor;
+    expL_Lmax.copyTo(*sampleProbs);
 
     if(logLikelihood)
     {
-        Mat expL;
-        exp(L, expL);
-        // note logLikelihood = log (sum_j exp(L_ij)) - 0.5 * dims * ln2Pi
-        *logLikelihood = std::log(sum(expL)[0]) - (double)(0.5 * dim * CV_LOG2PI);
+        double logWeightProbs = std::log(std::max(DBL_MIN, sum(*sampleProbs)[0]));
+        *logLikelihood = logWeightProbs;
     }
 }
 
diff --git a/modules/ml/test/test_emknearestkmeans.cpp b/modules/ml/test/test_emknearestkmeans.cpp
index d9b9460c89..31a9bee097 100644
--- a/modules/ml/test/test_emknearestkmeans.cpp
+++ b/modules/ml/test/test_emknearestkmeans.cpp
@@ -83,7 +83,7 @@ void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat&
     
     labels.create( data.rows, 1, labelType );
 
-    randn( data, Scalar::all(0.0), Scalar::all(1.0) );
+    randn( data, Scalar::all(-1.0), Scalar::all(1.0) );
     vector<Mat> means(sizes.size());
     for(int i = 0; i < _means.rows; i++)
         means[i] = _means.row(i);
@@ -381,7 +381,7 @@ int CV_EMTest::runCase( int caseIndex, const EM_Params& params,
         ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
         code = cvtest::TS::FAIL_INVALID_OUTPUT;
     }
-    else if( err > 0.006f )
+    else if( err > 0.008f )
     {
         ts->printf( cvtest::TS::LOG, "Case index %i : Bad accuracy (%f) on train data.\n", caseIndex, err );
         code = cvtest::TS::FAIL_BAD_ACCURACY;
@@ -401,7 +401,7 @@ int CV_EMTest::runCase( int caseIndex, const EM_Params& params,
         ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
         code = cvtest::TS::FAIL_INVALID_OUTPUT;
     }
-    else if( err > 0.006f )
+    else if( err > 0.008f )
     {
         ts->printf( cvtest::TS::LOG, "Case index %i : Bad accuracy (%f) on test data.\n", caseIndex, err );
         code = cvtest::TS::FAIL_BAD_ACCURACY;
@@ -505,7 +505,8 @@ protected:
     virtual void run( int /*start_from*/ )
     {
         int code = cvtest::TS::OK;
-        cv::EM em(2);
+        const int nclusters = 2;
+        cv::EM em(nclusters);
 
         Mat samples = Mat(3,1,CV_64FC1);
         samples.at<double>(0,0) = 1;