diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGmmEM.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGmmEM.scala
index 12be814a150b5..d59ba49ed1ba3 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGmmEM.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DenseGmmEM.scala
@@ -21,6 +21,13 @@ import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.mllib.clustering.GaussianMixtureModelEM
 import org.apache.spark.mllib.linalg.Vectors
 
+/**
+ * An example Gaussian Mixture Model EM app. Run with
+ * {{{
+ * ./bin/run-example org.apache.spark.examples.mllib.DenseGmmEM <input> <k> <covergenceTol>
+ * }}}
+ * If you use it as a template to create your own app, please use `spark-submit` to submit your app.
+ */
 object DenseGmmEM {
   def main(args: Array[String]): Unit = {
     if (args.length != 3) {
@@ -44,13 +51,15 @@ object DenseGmmEM {
       .run(data)
     
     for (i <- 0 until clusters.k) {
-      println("weight=%f mu=%s sigma=\n%s\n" format 
+      println("weight=%f\nmu=%s\nsigma=\n%s\n" format 
         (clusters.weight(i), clusters.mu(i), clusters.sigma(i)))
     }
     
+    println("Cluster labels:")
     val (responsibilityMatrix, clusterLabels) = clusters.predict(data)
     for (x <- clusterLabels.collect) {
       print(" " + x)
     }
+    println
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModelEM.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModelEM.scala
index 5ab654f609bd2..5c190120904b9 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModelEM.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModelEM.scala
@@ -26,6 +26,8 @@ import org.apache.spark.mllib.stat.impl.MultivariateGaussian
 import org.apache.spark.{Accumulator, AccumulatorParam, SparkContext}
 import org.apache.spark.SparkContext.DoubleAccumulatorParam
 
+import scala.collection.mutable.IndexedSeqView
+
 /**
  * This class performs expectation maximization for multivariate Gaussian
  * Mixture Models (GMMs).  A GMM represents a composite distribution of
@@ -51,6 +53,7 @@ class GaussianMixtureModelEM private (
   // Type aliases for convenience
   private type DenseDoubleVector = BreezeVector[Double]
   private type DenseDoubleMatrix = BreezeMatrix[Double]
+  private type VectorArrayView = IndexedSeqView[DenseDoubleVector, Array[DenseDoubleVector]]
   
   private type ExpectationSum = (
     Array[Double], // log-likelihood in index 0
@@ -80,10 +83,12 @@ class GaussianMixtureModelEM private (
   
   // compute cluster contributions for each input point
   // (U, T) => U for aggregation
-  private def computeExpectation(weights: Array[Double], dists: Array[MultivariateGaussian])
+  private def computeExpectation(
+      weights: Array[Double], 
+      dists: Array[MultivariateGaussian])
       (model: ExpectationSum, x: DenseDoubleVector): ExpectationSum = {
     val k = model._2.length
-    val p = (0 until k).map(i => eps + weights(i) * dists(i).pdf(x)).toArray
+    val p = weights.zip(dists).map { case (weight, dist) => eps + weight * dist.pdf(x) }
     val pSum = p.sum
     model._1(0) += math.log(pSum)
     val xxt = x * new Transpose(x)
@@ -106,7 +111,10 @@ class GaussianMixtureModelEM private (
   /** A default instance, 2 Gaussians, 100 iterations, 0.01 log-likelihood threshold */
   def this() = this(2, 0.01, 100)
   
-  /** Set the initial GMM starting point, bypassing the random initialization */
+  /** Set the initial GMM starting point, bypassing the random initialization.
+   *  You must call setK() prior to calling this method, and the condition
+   *  (gmm.k == this.k) must be met; failure will result in an IllegalArgumentException
+   */
   def setInitialGmm(gmm: GaussianMixtureModel): this.type = {
     if (gmm.k == k) {
       initialGmm = Some(gmm)
@@ -156,7 +164,7 @@ class GaussianMixtureModelEM private (
   
   /** Perform expectation maximization */
   def run(data: RDD[Vector]): GaussianMixtureModel = {
-    val ctx = data.sparkContext
+    val sc = data.sparkContext
     
     // we will operate on the data as breeze data
     val breezeData = data.map(u => u.toBreeze.toDenseVector).cache()
@@ -164,26 +172,22 @@ class GaussianMixtureModelEM private (
     // Get length of the input vectors
     val d = breezeData.first.length 
     
-    // gaussians will be array of (weight, mean, covariance) tuples.
+    // Determine initial weights and corresponding Gaussians.
     // If the user supplied an initial GMM, we use those values, otherwise
     // we start with uniform weights, a random mean from the data, and
     // diagonal covariance matrices using component variances
-    // derived from the samples 
-    var gaussians = initialGmm match {
-      case Some(gmm) => (0 until k).map{ i =>
-        (gmm.weight(i), gmm.mu(i).toBreeze.toDenseVector, gmm.sigma(i).toBreeze.toDenseMatrix)
-      }.toArray
+    // derived from the samples    
+    val (weights, gaussians) = initialGmm match {
+      case Some(gmm) => (gmm.weight, gmm.mu.zip(gmm.sigma).map{ case(mu, sigma) => 
+        new MultivariateGaussian(mu.toBreeze.toDenseVector, sigma.toBreeze.toDenseMatrix) 
+      }.toArray)
       
       case None => {
-        // For each Gaussian, we will initialize the mean as the average
-        // of some random samples from the data
         val samples = breezeData.takeSample(true, k * nSamples, scala.util.Random.nextInt)
-          
-        (0 until k).map{ i => 
-          (1.0 / k, 
-            vectorMean(samples.slice(i * nSamples, (i + 1) * nSamples)), 
-            initCovariance(samples.slice(i * nSamples, (i + 1) * nSamples)))
-        }.toArray
+        ((0 until k).map(_ => 1.0 / k).toArray, (0 until k).map{ i => 
+          val slice = samples.view(i * nSamples, (i + 1) * nSamples)
+          new MultivariateGaussian(vectorMean(slice), initCovariance(slice)) 
+        }.toArray)  
       }
     }
     
@@ -192,47 +196,36 @@ class GaussianMixtureModelEM private (
     
     var iter = 0
     do {
-      // pivot gaussians into weight and distribution arrays 
-      val weights = (0 until k).map(i => gaussians(i)._1).toArray
-      val dists = (0 until k).map{ i => 
-        new MultivariateGaussian(gaussians(i)._2, gaussians(i)._3)
-      }.toArray
-      
       // create and broadcast curried cluster contribution function
-      val compute = ctx.broadcast(computeExpectation(weights, dists)_)
+      val compute = sc.broadcast(computeExpectation(weights, gaussians)_)
       
       // aggregate the cluster contribution for all sample points
-      val sums = breezeData.aggregate(zeroExpectationSum(k, d))(compute.value, addExpectationSums)
-      
-      // Assignments to make the code more readable
-      val logLikelihood = sums._1(0)
-      val W = sums._2
-      val MU = sums._3
-      val SIGMA = sums._4
+      val (logLikelihood, wSums, muSums, sigmaSums) = 
+        breezeData.aggregate(zeroExpectationSum(k, d))(compute.value, addExpectationSums)
       
       // Create new distributions based on the partial assignments
       // (often referred to as the "M" step in literature)
-      gaussians = (0 until k).map{ i => 
-        val weight = W(i) / W.sum
-        val mu = MU(i) / W(i)
-        val sigma = SIGMA(i) / W(i) - mu * new Transpose(mu)
-        (weight, mu, sigma)
-      }.toArray
-      
+      val sumWeights = wSums.sum
+      for (i <- 0 until k) {
+        val mu = muSums(i) / wSums(i)
+        val sigma = sigmaSums(i) / wSums(i) - mu * new Transpose(mu)
+        weights(i) = wSums(i) / sumWeights
+        gaussians(i) = new MultivariateGaussian(mu, sigma)
+      }
+   
       llhp = llh // current becomes previous
-      llh = logLikelihood // this is the freshly computed log-likelihood
+      llh = logLikelihood(0) // this is the freshly computed log-likelihood
       iter += 1
     } while(iter < maxIterations && Math.abs(llh-llhp) > convergenceTol)
     
     // Need to convert the breeze matrices to MLlib matrices
-    val weights = (0 until k).map(i => gaussians(i)._1).toArray
-    val means   = (0 until k).map(i => Vectors.fromBreeze(gaussians(i)._2)).toArray
-    val sigmas  = (0 until k).map(i => Matrices.fromBreeze(gaussians(i)._3)).toArray
+    val means   = (0 until k).map(i => Vectors.fromBreeze(gaussians(i).mu)).toArray
+    val sigmas  = (0 until k).map(i => Matrices.fromBreeze(gaussians(i).sigma)).toArray
     new GaussianMixtureModel(weights, means, sigmas)
   }
     
   /** Average of dense breeze vectors */
-  private def vectorMean(x: Array[DenseDoubleVector]): DenseDoubleVector = {
+  private def vectorMean(x: VectorArrayView): DenseDoubleVector = {
     val v = BreezeVector.zeros[Double](x(0).length)
     x.foreach(xi => v += xi)
     v / x.length.asInstanceOf[Double] 
@@ -242,7 +235,7 @@ class GaussianMixtureModelEM private (
    * Construct matrix where diagonal entries are element-wise
    * variance of input vectors (computes biased variance)
    */
-  private def initCovariance(x: Array[DenseDoubleVector]): DenseDoubleMatrix = {
+  private def initCovariance(x: VectorArrayView): DenseDoubleMatrix = {
     val mu = vectorMean(x)
     val ss = BreezeVector.zeros[Double](x(0).length)
     val cov = BreezeMatrix.eye[Double](ss.length)
@@ -255,15 +248,18 @@ class GaussianMixtureModelEM private (
    * Given the input vectors, return the membership value of each vector
    * to all mixture components. 
    */
-  def predictClusters(points: RDD[Vector], mu: Array[Vector], sigma: Array[Matrix],
+  def predictClusters(
+      points: RDD[Vector], 
+      mu: Array[Vector], 
+      sigma: Array[Matrix],
       weight: Array[Double], k: Int): RDD[Array[Double]] = {
-    val ctx = points.sparkContext
-    val dists = ctx.broadcast{
+    val sc = points.sparkContext
+    val dists = sc.broadcast{
       (0 until k).map{ i => 
         new MultivariateGaussian(mu(i).toBreeze.toDenseVector, sigma(i).toBreeze.toDenseMatrix)
       }.toArray
     }
-    val weights = ctx.broadcast((0 until k).map(i => weight(i)).toArray)
+    val weights = sc.broadcast((0 until k).map(i => weight(i)).toArray)
     points.map{ x => 
       computeSoftAssignments(x.toBreeze.toDenseVector, dists.value, weights.value, k)
     }
@@ -272,11 +268,14 @@ class GaussianMixtureModelEM private (
   /**
    * Compute the partial assignments for each vector
    */
-  def computeSoftAssignments(pt: DenseDoubleVector, dists: Array[MultivariateGaussian],
-      weights: Array[Double], k: Int): Array[Double] = {
-    val p = (0 until k).map(i => eps + weights(i) * dists(i).pdf(pt)).toArray
+  private def computeSoftAssignments(
+      pt: DenseDoubleVector,
+      dists: Array[MultivariateGaussian],
+      weights: Array[Double],
+      k: Int): Array[Double] = {
+    val p = weights.zip(dists).map { case (weight, dist) => eps + weight * dist.pdf(pt) }
     val pSum = p.sum 
-    for(i<- 0 until k){
+    for (i <- 0 until k){
       p(i) /= pSum
     }
     p
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/stat/impl/MultivariateGaussian.scala b/mllib/src/main/scala/org/apache/spark/mllib/stat/impl/MultivariateGaussian.scala
index ab046a3acea05..d14adab09177e 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/stat/impl/MultivariateGaussian.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/stat/impl/MultivariateGaussian.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.mllib.stat.impl
 
 import breeze.linalg.{DenseVector => BreezeVector, DenseMatrix => BreezeMatrix}
-import breeze.linalg.{Transpose, det, inv}
+import breeze.linalg.{Transpose, det, pinv}
 
 /** 
    * Utility class to implement the density function for multivariate Gaussian distribution.
@@ -28,7 +28,7 @@ import breeze.linalg.{Transpose, det, inv}
 private[mllib] class MultivariateGaussian(
     val mu: BreezeVector[Double], 
     val sigma: BreezeMatrix[Double]) extends Serializable {
-  private val sigmaInv2 = inv(sigma) * -0.5
+  private val sigmaInv2 = pinv(sigma) * -0.5
   private val U = math.pow(2.0 * math.Pi, -mu.length / 2.0) * math.pow(det(sigma), -0.5)
     
   def pdf(x: BreezeVector[Double]): Double = {