Sparse Multi-Prototype Classification
Abstract
We present a new class of sparse multi-prototype classifiers, designed to combine the computational advantages of sparse predictors with the non-linear power of prototype-based classification techniques. This combination makes sparse multi-prototype models especially well-suited for resource constrained computational platforms, such as those found in IoT devices. We cast our supervised learning problem as a convex-concave saddle point problem and design a provably-fast algorithm to solve it. We complement our theoretical analysis with an empirical study that demonstrates the power of our methodology.
Cite
Text
Garg et al. "Sparse Multi-Prototype Classification." Conference on Uncertainty in Artificial Intelligence, 2018.Markdown
[Garg et al. "Sparse Multi-Prototype Classification." Conference on Uncertainty in Artificial Intelligence, 2018.](https://mlanthology.org/uai/2018/garg2018uai-sparse/)BibTeX
@inproceedings{garg2018uai-sparse,
title = {{Sparse Multi-Prototype Classification}},
author = {Garg, Vikas K. and Xiao, Lin and Dekel, Ofer},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2018},
pages = {704-714},
url = {https://mlanthology.org/uai/2018/garg2018uai-sparse/}
}