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Editor to share with you how to achieve principal component analysis of data dimensionality reduction in spark mllib. I hope you will gain something after reading this article. Let's discuss it together.

The running code is as follows: package spark.DataDimensionReductionimport org.apache.spark.mllib.linalg.Vectorsimport org.apache.spark.mllib.linalg.distributed.RowMatriximport org.apache.spark. {SparkConf, SparkContext} / * * data dimensionality reduction * Principal component analysis PCA * tries to reassemble the indicators with certain related rows (such as P indicators) into a new set of independent comprehensive indicators to replace the original indicators. In order to achieve the purpose of data dimensionality reduction * Created by eric on 16-7-24. * / object PCA {val conf = new SparkConf () / / create the environment variable .setMaster ("local") / / set the localization handler .setAppName ("PCA") / / set the name val sc = new SparkContext (conf) Def main (args: Array [String]) {val data = sc.textFile (". / src/main/spark/DataDimensionReduction/a.txt") .map (_ .split (") .map (_ .toDouble)) .map (line = > Vectors.dense (line)) val rm = new RowMatrix (data) val pc = rm.computePrincipalComponents (3) / / extract principal components Set the number of principal components to 3 val mx = rm.multiply (pc) / / create principal component matrix mx.rows.foreach (println)}}

A.txt

1 2 3 45 6 7 89 0 8 76 4 2 1 the results are as follows

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