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- trace_ratio(X, y, n_selected_features, **kwargs)
- This function implements the trace ratio criterion for feature selection
Input
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X: {numpy array}, shape (n_samples, n_features)
input data
y: {numpy array}, shape (n_samples,)
input class labels
n_selected_features: {int}
number of features to select
kwargs: {dictionary}
style: {string}
style == 'fisher', build between-class matrix and within-class affinity matrix in a fisher score way
style == 'laplacian', build between-class matrix and within-class affinity matrix in a laplacian score way
verbose: {boolean}
True if user want to print out the objective function value in each iteration, False if not
Output
------
feature_idx: {numpy array}, shape (n_features,)
the ranked (descending order) feature index based on subset-level score
feature_score: {numpy array}, shape (n_features,)
the feature-level score
subset_score: {float}
the subset-level score
Reference
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Feiping Nie et al. "Trace Ratio Criterion for Feature Selection." AAAI 2008.
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