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- calculate_obj(X, W, M, gamma)
- This function calculates the objective function of ls_l21 described in the paper
- construct_M(X, k, gamma)
- This function constructs the M matrix described in the paper
- udfs(X, **kwargs)
- This function implements l2,1-norm regularized discriminative feature
selection for unsupervised learning, i.e., min_W Tr(W^T M W) + gamma ||W||_{2,1}, s.t. W^T W = I
Input
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X: {numpy array}, shape (n_samples, n_features)
input data
kwargs: {dictionary}
gamma: {float}
parameter in the objective function of UDFS (default is 1)
n_clusters: {int}
Number of clusters
k: {int}
number of nearest neighbor
verbose: {boolean}
True if want to display the objective function value, false if not
Output
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W: {numpy array}, shape(n_features, n_clusters)
feature weight matrix
Reference
Yang, Yi et al. "l2,1-Norm Regularized Discriminative Feature Selection for Unsupervised Learning." AAAI 2012.
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