Pairwise Chi-squared distances

Given a list of affinity matrices, Wall, the number of clusters, return a matrix containing the NMIs between cluster assignments made with spectral clustering.

1 | ```
concordanceNetworkNMI(Wall, C)
``` |

`Wall` |
List of matrices. Each element of the list is a square, symmetric matrix that shows affinities of the data points from a certain view. |

`C` |
Number of clusters |

Returns an affinity matrix that represents the neighborhood graph of the data points.

Dr. Anna Goldenberg, Bo Wang, Aziz Mezlini, Feyyaz Demir

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
# How to use SNF with multiple views
# Load views into list "dataL"
data(dataL)
data(label)
# Set the other parameters
K = 20 # number of neighbours
alpha = 0.5 # hyperparameter in affinityMatrix
T = 20 # number of iterations of SNF
# Normalize the features in each of the views.
#dataL = lapply(dataL, standardNormalization)
# Calculate the distances for each view
distL = lapply(dataL, function(x) dist2(x, x))
# Construct the similarity graphs
affinityL = lapply(distL, function(x) affinityMatrix(x, K, alpha))
# an example of how to use concordanceNetworkNMI
Concordance_matrix = concordanceNetworkNMI(affinityL, 3);
## The output, Concordance_matrix,
## shows the concordance between the fused network and each individual network.
``` |

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