diff --git a/visualize.py b/visualize.py
index 90db73e..812f171 100644
--- a/visualize.py
+++ b/visualize.py
@@ -3,7 +3,7 @@ import os
 import matplotlib.pyplot as plt
 import numpy as np
 from keras.utils import plot_model
-from sklearn.decomposition import PCA
+from sklearn.decomposition import TruncatedSVD
 from sklearn.metrics import (
     auc, classification_report, confusion_matrix, fbeta_score, precision_recall_curve,
     roc_auc_score, roc_curve
@@ -146,13 +146,11 @@ def plot_training_curve(logs, key, path, dpi=600):
 
 
 def plot_embedding(domain_embedding, labels, path, dpi=600):
-    pca = PCA(n_components=2)
-    domain_reduced = pca.fit_transform(domain_embedding)
-    print(pca.explained_variance_ratio_)
-
+    svd = TruncatedSVD(n_components=2)
+    domain_reduced = svd.fit_transform(domain_embedding)
+    print(svd.explained_variance_ratio_)
     # use if draw subset of predictions
     # idx = np.random.choice(np.arange(len(domain_reduced)), 10000)
-
     plt.scatter(domain_reduced[:, 0],
                 domain_reduced[:, 1],
                 c=(labels * (1, 2)).sum(1).astype(int),