diff --git a/expui/SvdSignChoice.cc b/expui/SvdSignChoice.cc
index bb511e71..14a7b63e 100644
--- a/expui/SvdSignChoice.cc
+++ b/expui/SvdSignChoice.cc
@@ -109,4 +109,173 @@ namespace MSSA {
     
     // Done
   }
+
+  void SvdSignChoice
+  (const Eigen::MatrixXd& X,
+   Eigen::MatrixXd& U, const Eigen::VectorXd& S, const Eigen::MatrixXd& V)
+  {
+    // SDV dimensions
+    int I = U.rows();
+    int J = V.rows();
+    int K = S.size();
+    
+    // Dimensions
+    // ----------
+    // X is a [I x J] data matrix
+    // U is a [I x K] matrix
+    // S is a [K x 1] vector (diagonal matrix)
+    // V is a [J x K] matrix
+    
+    // Sanity checks
+    if (U.cols() != K)
+      throw std::invalid_argument("SvdSignChoice: U has wrong dimensions");
+    
+    if (V.cols() != K)
+      throw std::invalid_argument("SvdSignChoice: V has wrong dimensions");
+    
+    if (X.rows() != I || X.cols() != J)
+      throw std::invalid_argument("SvdSignChoice: X dimensions do not match SVD input");
+    
+    // Sign determination loop
+    //
+    Eigen::VectorXd sL(K), sR(K);
+    sL.setZero();
+    sR.setZero();
+    
+    // Working, non-const instance
+    auto S1 = S;
+    
+    // Get projections from left and right singular vectors onto data
+    // matrix
+    //
+    for (int k=0; k<K; k++) {
+      // Remove all but target dimension for numerical stability
+      S1(k) = 0.0;
+      auto Y = X - U * S1.asDiagonal() * V.transpose();
+
+      // Restore the value
+      S1(k) = S(k);
+
+      // d_j = U^T_k * Y_j
+      Eigen::VectorXd dL = Y.transpose() * U.col(k);
+
+      // sum of sgn(dL_j)*dL_j^2
+      sL(k) += dL.dot(dL.cwiseAbs());
+
+      // d_i = V^T_k * (Y^T)_i
+      Eigen::VectorXd dR = Y * V.col(k);
+
+      // sum of sgn(dR_i)*dR_i^2
+      sR(k) += dR.dot(dR.cwiseAbs());
+    }
+    
+    auto sgn = [](double val) -> int
+    {
+      return (0.0 < val) - (val < 0.0);
+    };
+    
+    // Determine and apply the sign correction
+    //
+    for (int k=0; k<K; k++) {
+      // If signs are opposite, flip the one with the smaller absolute
+      // value
+      //
+      if (sL(k)*sR(k) < 0.0) {
+	if (std::abs(sL(k)) < std::abs(sR(k)))
+	  sL(k) = -sL(k);
+	else
+	  sR(k) = -sR(k);
+      }
+      
+      // Apply
+      U.col(k) *= sgn(sL(k));
+    }
+    
+    // Done
+  }
+
+  void SvdSignChoice
+  (const Eigen::MatrixXd& X,
+   const Eigen::MatrixXd& U, const Eigen::VectorXd& S, Eigen::MatrixXd& V)
+  {
+    // SDV dimensions
+    int I = U.rows();
+    int J = V.rows();
+    int K = S.size();
+    
+    // Dimensions
+    // ----------
+    // X is a [I x J] data matrix
+    // U is a [I x K] matrix
+    // S is a [K x 1] vector (diagonal matrix)
+    // V is a [J x K] matrix
+    
+    // Sanity checks
+    if (U.cols() != K)
+      throw std::invalid_argument("SvdSignChoice: U has wrong dimensions");
+    
+    if (V.cols() != K)
+      throw std::invalid_argument("SvdSignChoice: V has wrong dimensions");
+    
+    if (X.rows() != I || X.cols() != J)
+      throw std::invalid_argument("SvdSignChoice: X dimensions do not match SVD input");
+    
+    // Sign determination loop
+    //
+    Eigen::VectorXd sL(K), sR(K);
+    sL.setZero();
+    sR.setZero();
+    
+    // Working, non-const instance
+    auto S1 = S;
+    
+    // Get projections from left and right singular vectors onto data
+    // matrix
+    //
+    for (int k=0; k<K; k++) {
+      // Remove all but target dimension for numerical stability
+      S1(k) = 0.0;
+      auto Y = X - U * S1.asDiagonal() * V.transpose();
+
+      // Restore the value
+      S1(k) = S(k);
+
+      // d_j = U^T_k * Y_j
+      Eigen::VectorXd dL = Y.transpose() * U.col(k);
+
+      // sum of sgn(dL_j)*dL_j^2
+      sL(k) += dL.dot(dL.cwiseAbs());
+
+      // d_i = V^T_k * (Y^T)_i
+      Eigen::VectorXd dR = Y * V.col(k);
+
+      // sum of sgn(dR_i)*dR_i^2
+      sR(k) += dR.dot(dR.cwiseAbs());
+    }
+    
+    auto sgn = [](double val) -> int
+    {
+      return (0.0 < val) - (val < 0.0);
+    };
+    
+    // Determine and apply the sign correction
+    //
+    for (int k=0; k<K; k++) {
+      // If signs are opposite, flip the one with the smaller absolute
+      // value
+      //
+      if (sL(k)*sR(k) < 0.0) {
+	if (std::abs(sL(k)) < std::abs(sR(k)))
+	  sL(k) = -sL(k);
+	else
+	  sR(k) = -sR(k);
+      }
+      
+      // Apply
+      V.col(k) *= sgn(sR(k));
+    }
+    
+    // Done
+  }
+
 }
diff --git a/expui/expMSSA.cc b/expui/expMSSA.cc
index 6283eb3a..2ff5079d 100644
--- a/expui/expMSSA.cc
+++ b/expui/expMSSA.cc
@@ -55,9 +55,15 @@
 
 namespace MSSA {
 
+  // Update sign of left vectors U only
   void SvdSignChoice
   (const Eigen::MatrixXd& X,
-   Eigen::MatrixXd& U, const Eigen::VectorXd& S, Eigen::MatrixXd& V);
+   Eigen::MatrixXd& U, const Eigen::VectorXd& S, const Eigen::MatrixXd& V);
+
+  // Update sign of right vectors V only
+  void SvdSignChoice
+  (const Eigen::MatrixXd& X,
+   const Eigen::MatrixXd& U, const Eigen::VectorXd& S, Eigen::MatrixXd& V);
 
   Eigen::MatrixXd expMSSA::wCorrKey(const Key& key)
   {
@@ -299,20 +305,14 @@ namespace MSSA {
 	  svd(YY, Eigen::ComputeThinU | Eigen::ComputeThinV);
 	S = svd.singularValues();
 	U = svd.matrixV();
-	if (useSignChoice) {
-	  auto V = svd.matrixU();
-	  SvdSignChoice(YY, V, S, U);
-	}
+	if (useSignChoice) SvdSignChoice(YY, svd.matrixU(), S, U);
       }
       else {			// Covariance matrix
 	Eigen::JacobiSVD<Eigen::MatrixXd>
 	  svd(cov, Eigen::ComputeThinU | Eigen::ComputeThinV);
 	S = svd.singularValues();
 	U = svd.matrixU();
-	if (useSignChoice) {
-	  auto V = svd.matrixV();
-	  SvdSignChoice(cov, U, S, V);
-	}
+	if (useSignChoice) SvdSignChoice(cov, U, S, svd.matrixV());
       }
     } else if (params["BDCSVD"]) {
       // -->Using BDC
@@ -322,20 +322,14 @@ namespace MSSA {
 	  svd(YY, Eigen::ComputeThinU | Eigen::ComputeThinV);
 	S = svd.singularValues();
 	U = svd.matrixV();
-	if (useSignChoice) {
-	  auto V = svd.matrixU();
-	  SvdSignChoice(YY, V, S, U);
-	}
+	if (useSignChoice) SvdSignChoice(YY, svd.matrixU(), S, U);
       }
       else {			// Covariance matrix
 	Eigen::BDCSVD<Eigen::MatrixXd>
 	  svd(cov, Eigen::ComputeThinU | Eigen::ComputeThinV);
 	S = svd.singularValues();
 	U = svd.matrixU();
-	if (useSignChoice) {
-	  auto V = svd.matrixV();
-	  SvdSignChoice(cov, U, S, V);
-	}
+	if (useSignChoice) SvdSignChoice(cov, U, S, svd.matrixV());
       }
     } else {
       // -->Use Random approximation algorithm from Halko, Martinsson,
@@ -345,28 +339,19 @@ namespace MSSA {
 	RedSVD::RedSVD<Eigen::MatrixXd> svd(YY, srank);
 	S = svd.singularValues();
 	U = svd.matrixV();
-	if (useSignChoice) {
-	  auto V = svd.matrixU();
-	  SvdSignChoice(YY, V, S, U);
-	}
+	if (useSignChoice) SvdSignChoice(YY, svd.matrixU(), S, U);
       }
       else {			// Covariance matrix
 	if (params["RedSym"]) {
 	  RedSVD::RedSymEigen<Eigen::MatrixXd> eigen(cov, srank);
 	  S = eigen.eigenvalues().reverse();
 	  U = eigen.eigenvectors().rowwise().reverse();
-	  if (useSignChoice) {
-	    Eigen::MatrixXd V = U.transpose();
-	    SvdSignChoice(cov, U, S, V);
-	  }
+	  if (useSignChoice) SvdSignChoice(cov, U, S, U.transpose());
 	} else {
 	  RedSVD::RedSVD<Eigen::MatrixXd> svd(cov, srank);
 	  S = svd.singularValues();
 	  U = svd.matrixU();
-	  if (useSignChoice) {
-	    Eigen::MatrixXd V = U.transpose();
-	    SvdSignChoice(cov, U, S, V);
-	  }
+	  if (useSignChoice) SvdSignChoice(cov, U, S, U.transpose());
 	}
       }
     }
@@ -1840,7 +1825,7 @@ namespace MSSA {
   }
 
 
-  expMSSA::expMSSA(const mssaConfig& config, int nW, int nPC, const std::string flags) : numW(nW), npc(nPC), trajectory(true), useSignChoice(false)
+  expMSSA::expMSSA(const mssaConfig& config, int nW, int nPC, const std::string flags) : numW(nW), npc(nPC), trajectory(true), useSignChoice(true)
   {
     // Parse the YAML string
     //