Supervised Categorical Metric Learning With Schatten p-Norms.

Area of Science:

• Machine Learning
• Data Science
• Computer Science

Background:

• Metric learning excels with numerical data but lags in categorical data applications.
• Existing methods for categorical data lack efficiency and optimal prediction accuracy.

Purpose of the Study:

• To introduce Categorical Projected Metric Learning (CPML), an efficient method for categorical data.
• To enhance prediction accuracy and reduce computational time in metric learning for categorical datasets.

Main Methods:

• Utilizing a value distance metric for data representation.
• Developing novel distance metrics tailored for categorical data.
• Generalizing regularizers via the Schatten p-norm and providing generalization bounds.

Main Results:

• CPML achieves state-of-the-art prediction accuracy on categorical data.
• The proposed method significantly reduces computational time compared to existing approaches.
• A new generalization bound complements existing metric learning bounds.

Conclusions:

• CPML effectively addresses the challenges of metric learning in categorical data.
• The method offers a computationally efficient and highly accurate solution.
• The generalization bound contributes theoretical insights to metric learning research.