Sparse Multi-Prototype Classification

We introduce a new class of sparse multiprototype classifiers, designed to combine the computational advantages of sparse predictors with the non-linear power of prototype-based classification techniques. This combination makes sparse multiprototype models especially well-suited for resource constrained computational platforms, such as the IoT devices. We cast our supervised learning problem as a convexconcave saddle point problem and design a provably-fast algorithm to solve it. We complement our theoretical analysis with an empirical study that demonstrates the merits of our methodology.

Authors

• Vikas K. Garg 0001
• Lin Xiao
• Ofer Dekel

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