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diff --git a/src/de/lmu/ifi/dbs/elki/math/linearalgebra/randomprojections/RandomSubsetProjectionFamily.java b/src/de/lmu/ifi/dbs/elki/math/linearalgebra/randomprojections/RandomSubsetProjectionFamily.java
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+package de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections;
+
+/*
+ This file is part of ELKI:
+ Environment for Developing KDD-Applications Supported by Index-Structures
+
+ Copyright (C) 2013
+ 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.Arrays;
+import java.util.Random;
+
+import de.lmu.ifi.dbs.elki.data.NumberVector;
+import de.lmu.ifi.dbs.elki.utilities.RandomFactory;
+import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
+
+/**
+ * Random projection family based on selecting random features.
+ * 
+ * The basic idea of using this for data mining should probably be attributed to
+ * L. Breiman, who used it to improve the performance of predictors in an
+ * ensemble.
+ * 
+ * Reference:
+ * <p>
+ * L. Breiman<br />
+ * Bagging predictors<br />
+ * Machine learning 24.2
+ * </p>
+ * 
+ * @author Erich Schubert
+ */
+@Reference(authors = "L. Breiman", title = "Bagging predictors", booktitle = "Machine learning 24.2", url = "http://dx.doi.org/10.1007/BF00058655")
+public class RandomSubsetProjectionFamily extends AbstractRandomProjectionFamily {
+  /**
+   * Constructor.
+   * 
+   * @param random Random generator.
+   */
+  public RandomSubsetProjectionFamily(RandomFactory random) {
+    super(random);
+  }
+
+  @Override
+  public Projection generateProjection(int idim, int odim) {
+    int[] dims;
+    if (odim < idim) {
+      dims = Arrays.copyOf(randomPermutation(range(0, idim), random), odim);
+    } else if (odim == idim) {
+      dims = randomPermutation(range(0, idim), random);
+    } else {
+      int mdim = idim;
+      while (mdim < odim) {
+        mdim += idim;
+      }
+      dims = new int[mdim];
+      for (int i = 0; i < mdim; i++) {
+        dims[i] = i % idim;
+      }
+      dims = Arrays.copyOf(randomPermutation(dims, random), odim);
+    }
+    return new SubsetProjection(dims);
+  }
+
+  /**
+   * Initialize an integer value range.
+   * 
+   * FIXME: move to shared code.
+   * 
+   * @param start Starting value
+   * @param end End value (exclusive)
+   * @return Array of integers start..end, excluding end.
+   */
+  public static int[] range(int start, int end) {
+    int[] out = new int[end - start];
+    for (int i = 0, j = start; j < end; i++, j++) {
+      out[i] = j;
+    }
+    return out;
+  }
+
+  /**
+   * Perform a random permutation of the array, in-place.
+   * 
+   * Knuth / Fisher-Yates style shuffle
+   * 
+   * FIXME: move to shared code.
+   * 
+   * @param existing Existing array
+   * @param random Random generator.
+   * @return Same array.
+   */
+  public static int[] randomPermutation(final int[] out, Random random) {
+    for (int i = out.length - 1; i > 0; i--) {
+      // Swap with random preceeding element.
+      int ri = random.nextInt(i + 1);
+      int tmp = out[ri];
+      out[ri] = out[i];
+      out[i] = tmp;
+    }
+    return out;
+  }
+
+  /**
+   * Random subset projection.
+   * 
+   * @author Erich Schubert
+   */
+  public static class SubsetProjection implements Projection {
+    /**
+     * Input dimensions.
+     */
+    private int[] dims;
+
+    /**
+     * Constructor.
+     * 
+     * @param dims Data permutation.
+     */
+    public SubsetProjection(int[] dims) {
+      this.dims = dims;
+    }
+
+    @Override
+    public double[] project(NumberVector<?> in) {
+      double[] buf = new double[dims.length];
+      for (int i = 0; i < dims.length; i++) {
+        buf[i] = in.doubleValue(dims[i]);
+      }
+      return buf;
+    }
+
+    @Override
+    public int getOutputDimensionality() {
+      return dims.length;
+    }
+  }
+
+  /**
+   * Parameterization class.
+   * 
+   * @author Erich Schubert
+   * 
+   * @apiviz.exclude
+   */
+  public static class Parameterizer extends AbstractRandomProjectionFamily.Parameterizer {
+    @Override
+    protected RandomSubsetProjectionFamily makeInstance() {
+      return new RandomSubsetProjectionFamily(random);
+    }
+  }
+}