I implement a simple PCA wrapper for easy transform of vectors by PCA for example LabeledPoint or another complicated structure.

Example of usage:
```
  import org.apache.spark.mllib.regression.LinearRegressionWithSGD
  import org.apache.spark.mllib.regression.LabeledPoint
  import org.apache.spark.mllib.linalg.Vectors
  import org.apache.spark.mllib.feature.PCA

  val data = sc.textFile("data/mllib/ridge-data/lpsa.data").map { line =>
    val parts = line.split(',')
    LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
  }.cache()

  val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L)
  val training = splits(0).cache()
  val test = splits(1)

  val pca = PCA.create(training.first().features.size/2, data.map(_.features))
  val training_pca = training.map(p => p.copy(features = pca.transform(p.features)))
  val test_pca = test.map(p => p.copy(features = pca.transform(p.features)))

  val numIterations = 100
  val model = LinearRegressionWithSGD.train(training, numIterations)
  val model_pca = LinearRegressionWithSGD.train(training_pca, numIterations)

  val valuesAndPreds = test.map { point =>
    val score = model.predict(point.features)
    (score, point.label)
  }

  val valuesAndPreds_pca = test_pca.map { point =>
    val score = model_pca.predict(point.features)
    (score, point.label)
  }

  val MSE = valuesAndPreds.map{case(v, p) => math.pow((v - p), 2)}.mean()
  val MSE_pca = valuesAndPreds_pca.map{case(v, p) => math.pow((v - p), 2)}.mean()

  println("Mean Squared Error = " + MSE)
  println("PCA Mean Squared Error = " + MSE_pca)
```

Author: Kirill A. Korinskiy <catap@catap.ru>
Author: Joseph K. Bradley <joseph@databricks.com>

Closes #4304 from catap/pca and squashes the following commits:

501bcd9 [Joseph K. Bradley] Small updates: removed k from Java-friendly PCA fit().  In PCASuite, converted results to set for comparison. Added an error message for bad k in PCA.
9dcc02b [Kirill A. Korinskiy] [SPARK-5521] fix scala style
1892a06 [Kirill A. Korinskiy] [SPARK-5521] PCA wrapper for easy transform vectors