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Various clustering algorithm implementations for all primitive types including random, random forest, K-Means (Exact, Hierarchical and Approximate), ...
/**
* Copyright (c) 2011, The University of Southampton and the individual contributors.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification,
* are permitted provided that the following conditions are met:
*
* * Redistributions of source code must retain the above copyright notice,
* this list of conditions and the following disclaimer.
*
* * Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* * Neither the name of the University of Southampton nor the names of its
* contributors may be used to endorse or promote products derived from this
* software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
* ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
* ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
package org.openimaj.ml.clustering.spectral;
import java.util.ArrayList;
import java.util.List;
import org.openimaj.citation.annotation.Reference;
import org.openimaj.citation.annotation.ReferenceType;
import org.openimaj.math.matrix.MatlibMatrixUtils;
import org.openimaj.ml.clustering.IndexClusters;
import org.openimaj.ml.clustering.MultiviewSimilarityClusterer;
import org.openimaj.util.pair.IndependentPair;
import ch.akuhn.matrix.DenseMatrix;
import ch.akuhn.matrix.Matrix;
import ch.akuhn.matrix.SparseMatrix;
/**
*
* @author Sina Samangooei ([email protected])
*
*/
@Reference(
type = ReferenceType.Incollection,
author = { "Abhishek Kumar", "Piyush Rai", "Hal Daume III" },
title = "Co-regularized Multi-view Spectral Clustering",
year = "2011",
booktitle = "Advances in Neural Information Processing Systems 24",
pages = { "1413", "", "1421" },
editor = { "J. Shawe-Taylor", "R.S. Zemel", "P. Bartlett", "F.C.N. Pereira", "K.Q. Weinberger" })
public class DoubleMultiviewSpectralClustering implements MultiviewSimilarityClusterer {
private MultiviewSpectralClusteringConf conf;
/**
* @param conf
* cluster the eigen vectors
*/
public DoubleMultiviewSpectralClustering(MultiviewSpectralClusteringConf conf) {
this.conf = conf;
}
@Override
public IndexClusters cluster(List data) {
final DoubleSpectralClustering dsp = new DoubleSpectralClustering(conf);
if (data.size() == 1) {
return dsp.cluster(data.get(0));
}
// Solve the spectral clustering for each view
final ArrayList> answers = new ArrayList>(data.size());
PreparedSpectralClustering prep = new PreparedSpectralClustering(conf);
for (int i = 0; i < data.size(); i++) {
answers.add(prep.bestCols(dsp.spectralCluster(data.get(i))));
}
while (!conf.stop.stop(answers)) {
for (int i = 0; i < answers.size(); i++) {
// L
final SparseMatrix laplacian = dsp.laplacian(data.get(i));
// lambda * (Sum_w!=v u_w . u_w^T)
SparseMatrix ujujSum = null;
for (int j = 0; j < answers.size(); j++) {
if (i == j)
continue;
final Matrix uj = new DenseMatrix(answers.get(j).secondObject());
final SparseMatrix ujuj = MatlibMatrixUtils.dotProductTranspose(uj, uj,
new SparseMatrix(uj.rowCount(), uj.rowCount()));
if (ujujSum == null) {
ujujSum = ujuj;
}
else {
MatlibMatrixUtils.plusInplace(ujujSum, ujuj);
}
}
// L + lambda * (Sum_w!=v u_w . u_w^T)
MatlibMatrixUtils.plusInplace(laplacian, MatlibMatrixUtils.scaleInplace(ujujSum, conf.lambda));
// eig
answers.add(i, prep.bestCols(dsp.laplacianEigenVectors(laplacian)));
}
}
// Concatenate the eigen spaces and cluster using the conf clusterer
// return
// dsp.eigenspaceCluster(ArrayUtils.concatenate(answers.toArray(new
// double[answers.size()][][])));
return null;
}
@Override
public int[][] performClustering(List data) {
return this.cluster(data).clusters();
}
}