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- construct_W(X, **kwargs)
- Construct the affinity matrix W through different ways
Notes
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if kwargs is null, use the default parameter settings;
if kwargs is not null, construct the affinity matrix according to parameters in kwargs
Input
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X: {numpy array}, shape (n_samples, n_features)
input data
kwargs: {dictionary}
parameters to construct different affinity matrix W:
y: {numpy array}, shape (n_samples, 1)
the true label information needed under the 'supervised' neighbor mode
metric: {string}
choices for different distance measures
'euclidean' - use euclidean distance
'cosine' - use cosine distance (default)
neighbor_mode: {string}
indicates how to construct the graph
'knn' - put an edge between two nodes if and only if they are among the
k nearest neighbors of each other (default)
'supervised' - put an edge between two nodes if they belong to same class
and they are among the k nearest neighbors of each other
weight_mode: {string}
indicates how to assign weights for each edge in the graph
'binary' - 0-1 weighting, every edge receives weight of 1 (default)
'heat_kernel' - if nodes i and j are connected, put weight W_ij = exp(-norm(x_i - x_j)/2t^2)
this weight mode can only be used under 'euclidean' metric and you are required
to provide the parameter t
'cosine' - if nodes i and j are connected, put weight cosine(x_i,x_j).
this weight mode can only be used under 'cosine' metric
k: {int}
choices for the number of neighbors (default k = 5)
t: {float}
parameter for the 'heat_kernel' weight_mode
fisher_score: {boolean}
indicates whether to build the affinity matrix in a fisher score way, in which W_ij = 1/n_l if yi = yj = l;
otherwise W_ij = 0 (default fisher_score = false)
reliefF: {boolean}
indicates whether to build the affinity matrix in a reliefF way, NH(x) and NM(x,y) denotes a set of
k nearest points to x with the same class as x, and a different class (the class y), respectively.
W_ij = 1 if i = j; W_ij = 1/k if x_j \in NH(x_i); W_ij = -1/(c-1)k if x_j \in NM(x_i, y) (default reliefF = false)
Output
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W: {sparse matrix}, shape (n_samples, n_samples)
output affinity matrix W
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