@Reference(authors="L. Breiman", title="Bagging predictors", booktitle="Machine learning 24.2", url="https://doi.org/10.1007/BF00058655", bibkey="DBLP:journals/ml/Breiman96b") @Alias(value="de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.RandomSubsetProjectionFamily") public class RandomSubsetProjectionFamily extends AbstractRandomProjectionFamily
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:
L. Breiman
Bagging predictors
Machine learning 24.2
Modifier and Type | Class and Description |
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static class |
RandomSubsetProjectionFamily.Parameterizer
Parameterization class.
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static class |
RandomSubsetProjectionFamily.SubsetProjection
Random subset projection.
|
AbstractRandomProjectionFamily.MatrixProjection
RandomProjectionFamily.Projection
random
Constructor and Description |
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RandomSubsetProjectionFamily(RandomFactory random)
Constructor.
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Modifier and Type | Method and Description |
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RandomProjectionFamily.Projection |
generateProjection(int idim,
int odim)
Generate a projection matrix for the given dimensionalities.
|
static int[] |
randomPermutation(int[] out,
java.util.Random random)
Perform a random permutation of the array, in-place.
|
private static int[] |
sequence(int e)
Generate the integer sequence 0 .. e-1
TODO: move to shared code.
|
public RandomSubsetProjectionFamily(RandomFactory random)
random
- Random generator.public RandomProjectionFamily.Projection generateProjection(int idim, int odim)
RandomProjectionFamily
idim
- Input Dimensionalityodim
- Output Dimensionalitypublic static int[] randomPermutation(int[] out, java.util.Random random)
out
- Existing output arrayrandom
- Random generator.private static int[] sequence(int e)
TODO: move to shared code.
e
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