Applications¶
Improving similarity learning classifiers¶
Learning a distance that fits the data properly will improve the accuracy of distance-based classifiers.
Dimensionality reduction¶
Many of the distance metric learning algorithms can learn projections onto low dimensional spaces. Dimensionality reduction improves the classifier eficiency, reduces overfitting and avoids problems such a the curse of dimensionality present in some similarity classifiers.