PCA Implementation in Java [closed]

Question

I need implementation of PCA in Java. I am interested in finding something that's well documented, practical and easy to use. Any recommendations?

Answer 1

There are now a number of Principal Component Analysis implementations for Java.

1. Apache Spark: https://spark.apache.org/docs/2.1.0/mllib-dimensionality-reduction.html#principal-component-analysis-pca

   SparkConf conf = new SparkConf().setAppName("PCAExample").setMaster("local");
   try (JavaSparkContext sc = new JavaSparkContext(conf)) {
       //Create points as Spark Vectors
       List<Vector> vectors = Arrays.asList(
               Vectors.dense( -1.0, -1.0 ),
               Vectors.dense( -1.0, 1.0 ),
               Vectors.dense( 1.0, 1.0 ));
   
       //Create Spark MLLib RDD
       JavaRDD<Vector> distData = sc.parallelize(vectors);
       RDD<Vector> vectorRDD = distData.rdd();
   
       //Execute PCA Projection to 2 dimensions
       PCA pca = new PCA(2); 
       PCAModel pcaModel = pca.fit(vectorRDD);
       Matrix matrix = pcaModel.pc();
   }
   

2. ND4J: https://deeplearning4j.org/api/latest/org/nd4j/linalg/dimensionalityreduction/PCA.html

   //Create points as NDArray instances
   List<INDArray> ndArrays = Arrays.asList(
           new NDArray(new float [] {-1.0F, -1.0F}),
           new NDArray(new float [] {-1.0F, 1.0F}),
           new NDArray(new float [] {1.0F, 1.0F}));
   
   //Create matrix of points (rows are observations; columns are features)
   INDArray matrix = new NDArray(ndArrays, new int [] {3,2});
   
   //Execute PCA - again to 2 dimensions
   INDArray factors = PCA.pca_factor(matrix, 2, false);
   

3. Apache Commons Math (single threaded; no framework)

   //create points in a double array
   double[][] pointsArray = new double[][] { 
       new double[] { -1.0, -1.0 }, 
       new double[] { -1.0, 1.0 },
       new double[] { 1.0, 1.0 } };
   
   //create real matrix
   RealMatrix realMatrix = MatrixUtils.createRealMatrix(pointsArray);
   
   //create covariance matrix of points, then find eigenvectors
   //see https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues
   
   Covariance covariance = new Covariance(realMatrix);
   RealMatrix covarianceMatrix = covariance.getCovarianceMatrix();
   EigenDecomposition ed = new EigenDecomposition(covarianceMatrix);
   

Note, Singular Value Decomposition, which can also be used to find Principal Components, has equivalent implementations.

Answer 2

Smile is a full-fledged ML library for java. You give its PCA implementation a try. Please see: https://haifengl.github.io/smile/api/java/smile/projection/PCA.html

There is also PCA tutorial with Smile but the tutorial uses Scala.