This class implements spectral clustering.
More...
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| | ClusteringSpectral (const Matrix &affinityMatrix, const unsigned int numberCluster) |
| | Determines the symmetric Laplacian matrix of input given and computes the eigen system of the Laplacian matrix. More...
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| std::vector< Indices32 > | clusterRotation (const unsigned int iterations=100u, const Scalar convergenceThreshold=Scalar(0.0001)) |
| | Performances the actual clustering. More...
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This class implements spectral clustering.
◆ ClusteringSpectral()
| Ocean::ClusteringSpectral::ClusteringSpectral |
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const Matrix & |
affinityMatrix, |
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const unsigned int |
numberCluster |
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) |
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Determines the symmetric Laplacian matrix of input given and computes the eigen system of the Laplacian matrix.
- Parameters
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| affinityMatrix | a square weight matrix derived from similarity matrix (must be non-negative) |
| numberCluster | number of cluster to be returned (0, infinity) |
◆ clusterRotation()
| std::vector<Indices32> Ocean::ClusteringSpectral::clusterRotation |
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const unsigned int |
iterations = 100u, |
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const Scalar |
convergenceThreshold = Scalar(0.0001) |
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) |
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Performances the actual clustering.
The algorithm is implemented according to "Huang et al. Spectral rotation versus K-means in spectral clustering. AAAI'13".
- Parameters
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| iterations | Upper limit of iterations to be performed [1, infinity) |
| convergenceThreshold | Differential threshold used as convergence criteria (0, infinity) |
- Returns
- Indices of cluster elements (number of cluster was given in constructor)
◆ determineLaplacianMatrix()
| static Matrix Ocean::ClusteringSpectral::determineLaplacianMatrix |
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const Matrix & |
affinityMatrix | ) |
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staticprotected |
Determines unnormalized graph Laplacian matrix.
L = D - W. W is the weight matrix and D is a diagonal matrix containing the row sum of W
- Parameters
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| affinityMatrix | a square weight matrix derived from similarity matrix (must be non-negative) |
- Returns
- Laplacian matrix
◆ determineRandomWalkLaplacianMatrix()
template<bool tSimplify>
| static Matrix Ocean::ClusteringSpectral::determineRandomWalkLaplacianMatrix |
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const Matrix & |
affinityMatrix | ) |
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staticprotected |
Determines normalized graph Laplacian matrix closely connected to a random walk.
L = I - D^{-1} * W. W is the weight matrix, I is the identity matrix and D is a diagonal matrix containing the row sum of W
- Template Parameters
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| tSimplify | indicating whether a simplified version is used, then substraction with identity matrix is obmitted. Mathematically wrong but commonly used |
- Parameters
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| affinityMatrix | a square weight matrix derived from similarity matrix (must be non-negative) |
- Returns
- Laplacian matrix
◆ determineSymmetricLaplacianMatrix()
template<bool tSimplify>
| static Matrix Ocean::ClusteringSpectral::determineSymmetricLaplacianMatrix |
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const Matrix & |
affinityMatrix | ) |
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staticprotected |
Determines symmetric (normalized) graph Laplacian matrix.
L = I - D^{-1/2} * W * D^{-1/2}. W is the weight matrix, I is the identity matrix and D is a diagonal matrix containing the row sum of W
- Template Parameters
-
| tSimplify | indicating whether a simplified version is used, then substraction with identity matrix is obmitted. Mathematically wrong but commonly used |
- Parameters
-
| affinityMatrix | a square weight matrix derived from similarity matrix (must be non-negative) |
- Returns
- Laplacian matrix
◆ pairSortDescending()
template<typename T >
| bool Ocean::ClusteringSpectral::pairSortDescending |
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const std::pair< T, MatrixT< T >> & |
firstElem, |
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const std::pair< T, MatrixT< T >> & |
secondElem |
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) |
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inlinestaticprotected |
Sorts a list of eigenvectors with corresponding eigenvalues in descending order.
- Parameters
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| firstElem | First pair of eigenvalue and eigenvector |
| secondElem | Second pair of eigenvalue and eigenvector |
- Returns
- first eigenvalue is greater than eigenvalue of second element
◆ reducedEigenvectors
| Matrix Ocean::ClusteringSpectral::reducedEigenvectors |
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protected |
Column matrix of eigen vectors ordered by eigen values and reduced to [numberCluster] vectors.
The documentation for this class was generated from the following file:
1.9.1