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diff --git a/src/Witness_complex/include/gudhi/Landmark_choice_by_random_point.h b/src/Witness_complex/include/gudhi/Landmark_choice_by_random_point.h
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+/*    This file is part of the Gudhi Library. The Gudhi library
+ *    (Geometric Understanding in Higher Dimensions) is a generic C++
+ *    library for computational topology.
+ *
+ *    Author(s):       Siargey Kachanovich
+ *
+ *    Copyright (C) 2015  INRIA Sophia Antipolis-Méditerranée (France)
+ *
+ *    This program is free software: you can redistribute it and/or modify
+ *    it under the terms of the GNU 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 General Public License for more details.
+ *
+ *    You should have received a copy of the GNU General Public License
+ *    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
+#ifndef LANDMARK_CHOICE_BY_RANDOM_POINT_H_
+#define LANDMARK_CHOICE_BY_RANDOM_POINT_H_
+
+#include <queue>  // for priority_queue<>
+#include <utility>  // for pair<>
+#include <vector>
+#include <set>
+
+namespace Gudhi {
+
+namespace witness_complex {
+
+/** 
+ *  \class Landmark_choice_by_random_point
+ *  \brief The class `Landmark_choice_by_random_point` allows to construct the matrix
+ *  of closest landmarks per witness by iteratively choosing a random non-chosen witness
+ *  as a new landmark. 
+ *  \ingroup witness_complex
+ */
+
+class Landmark_choice_by_random_point {
+ public:
+  /** \brief Landmark choice strategy by taking random vertices for landmarks.
+   *  \details It chooses nbL distinct landmarks from a random access range `points`
+   *  and outputs a matrix {witness}*{closest landmarks} in knn.
+   */
+
+  template <typename KNearestNeighbours,
+  typename Point_random_access_range>
+  Landmark_choice_by_random_point(Point_random_access_range const &points,
+                                  int nbL,
+                                  KNearestNeighbours &knn) {
+    int nbP = points.end() - points.begin();
+    assert(nbP >= nbL);
+    std::set<int> landmarks;
+    int current_number_of_landmarks = 0;  // counter for landmarks
+
+    // TODO(SK) Consider using rand_r(...) instead of rand(...) for improved thread safety
+    int chosen_landmark = rand() % nbP;
+    for (current_number_of_landmarks = 0; current_number_of_landmarks != nbL; current_number_of_landmarks++) {
+      while (landmarks.find(chosen_landmark) != landmarks.end())
+        chosen_landmark = rand() % nbP;
+      landmarks.insert(chosen_landmark);
+    }
+
+    int dim = points.begin()->size();
+    typedef std::pair<double, int> dist_i;
+    typedef bool (*comp)(dist_i, dist_i);
+    knn = KNearestNeighbours(nbP);
+    for (int points_i = 0; points_i < nbP; points_i++) {
+      std::priority_queue<dist_i, std::vector<dist_i>, comp> l_heap([&](dist_i j1, dist_i j2) {
+        return j1.first > j2.first;
+      });
+      std::set<int>::iterator landmarks_it;
+      int landmarks_i = 0;
+      for (landmarks_it = landmarks.begin(), landmarks_i = 0; landmarks_it != landmarks.end();
+           landmarks_it++, landmarks_i++) {
+        dist_i dist = std::make_pair(euclidean_distance(points[points_i], points[*landmarks_it]), landmarks_i);
+        l_heap.push(dist);
+      }
+      for (int i = 0; i < dim + 1; i++) {
+        dist_i dist = l_heap.top();
+        knn[points_i].push_back(dist.second);
+        l_heap.pop();
+      }
+    }
+  }
+};
+
+}  // namespace witness_complex
+
+}  // namespace Gudhi
+
+#endif  // LANDMARK_CHOICE_BY_RANDOM_POINT_H_