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diff --git a/elki/src/main/java/de/lmu/ifi/dbs/elki/math/linearalgebra/randomprojections/AbstractRandomProjectionFamily.java b/elki/src/main/java/de/lmu/ifi/dbs/elki/math/linearalgebra/randomprojections/AbstractRandomProjectionFamily.java
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+++ b/elki/src/main/java/de/lmu/ifi/dbs/elki/math/linearalgebra/randomprojections/AbstractRandomProjectionFamily.java
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+package de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections;
+
+import java.util.Arrays;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2015
+ Ludwig-Maximilians-Universität München
+ Lehr- und Forschungseinheit für Datenbanksysteme
+ ELKI Development Team
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU Affero General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ GNU Affero General Public License for more details.
+
+ You should have received a copy of the GNU Affero General Public License
+ along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
+import java.util.Random;
+
+import de.lmu.ifi.dbs.elki.data.NumberVector;
+import de.lmu.ifi.dbs.elki.data.SparseNumberVector;
+import de.lmu.ifi.dbs.elki.math.MathUtil;
+import de.lmu.ifi.dbs.elki.math.random.RandomFactory;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.OptionID;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameterization.Parameterization;
+import de.lmu.ifi.dbs.elki.utilities.optionhandling.parameters.RandomParameter;
+
+/**
+ * Abstract base class for random projection families.
+ *
+ * @author Erich Schubert
+ */
+public abstract class AbstractRandomProjectionFamily implements RandomProjectionFamily {
+  /**
+   * Random generator.
+   */
+  protected Random random;
+
+  /**
+   * Constructor.
+   */
+  public AbstractRandomProjectionFamily(RandomFactory random) {
+    super();
+    this.random = random.getSingleThreadedRandom();
+  }
+
+  /**
+   * Class to project using a matrix multiplication. This class is optimized for
+   * dense vector multiplications. In other words, the row dimensionality is the
+   * output dimensionality, the column dimensionality is the input
+   * dimensionality.
+   *
+   * It is <b>not thread safe</b> because it uses an internal buffer to store a
+   * local copy of the vector.
+   *
+   * @author Erich Schubert
+   */
+  public static class MatrixProjection implements Projection {
+    /**
+     * Projection matrix.
+     */
+    double[][] matrix;
+
+    /**
+     * Shared buffer to use during projections.
+     */
+    private double[] buf;
+
+    /**
+     * Constructor.
+     *
+     * @param matrix Projection matrix ([output dim][input dim]).
+     */
+    public MatrixProjection(double[][] matrix) {
+      super();
+      this.matrix = matrix;
+      this.buf = new double[matrix.length];
+    }
+
+    @Override
+    public double[] project(NumberVector in) {
+      return project(in, new double[matrix.length]);
+    }
+
+    @Override
+    public double[] project(NumberVector in, double[] ret) {
+      if(in instanceof SparseNumberVector) {
+        return projectSparse((SparseNumberVector) in, ret);
+      }
+      final int dim = MathUtil.min(buf.length, in.getDimensionality());
+      assert (ret.length >= matrix.length) : "Output buffer too small!";
+      // Copy vector into local buffer
+      for(int i = 0; i < dim; i++) {
+        buf[i] = in.doubleValue(i);
+      }
+      // Iterate over output dimensions:
+      for(int o = 0; o < matrix.length; o++) {
+        final double[] row = matrix[o];
+        double v = 0.;
+        for(int i = 0; i < dim; i++) {
+          v += row[i] * buf[i]; // Rows and input are aligned.
+        }
+        ret[o] = v;
+      }
+      // Fill excess dimensions.
+      for(int d = matrix.length; d < ret.length; d++) {
+        ret[d] = 0;
+      }
+      return ret;
+    }
+
+    /**
+     * Project, exploiting sparsity; but the transposed matrix layout would have
+     * been better. For projections where you expect sparse input, consider the
+     * opposite.
+     *
+     * @param in Input vector
+     * @param ret Projection buffer
+     * @return Projected data.
+     */
+    private double[] projectSparse(SparseNumberVector in, double[] ret) {
+      Arrays.fill(ret, 0);
+      for(int iter = in.iter(); in.iterValid(iter); iter = in.iterAdvance(iter)) {
+        final int i = in.iterDim(iter);
+        final double val = in.iterDoubleValue(iter);
+        for(int o = 0; o < ret.length; o++) {
+          ret[o] += val * matrix[o][i]; // Not aligned.
+        }
+      }
+      return ret;
+    }
+
+    @Override
+    public int getOutputDimensionality() {
+      return matrix.length;
+    }
+  }
+
+  /**
+   * Parameterization interface (with the shared parameters)
+   *
+   * @author Erich Schubert
+   *
+   * @apiviz.exclude
+   */
+  public abstract static class Parameterizer extends AbstractParameterizer {
+    /**
+     * Parameter for the random generator.
+     */
+    public static final OptionID RANDOM_ID = new OptionID("randomproj.random", "Random generator seed.");
+
+    /**
+     * Random generator.
+     */
+    protected RandomFactory random;
+
+    @Override
+    protected void makeOptions(Parameterization config) {
+      super.makeOptions(config);
+      RandomParameter rndP = new RandomParameter(RANDOM_ID);
+      if(config.grab(rndP)) {
+        random = rndP.getValue();
+      }
+    }
+  }
+}