@article{Moreira Cestari_Mello_2020, title={Random Projections and α-Shape to Support the Kernel Design (Student Abstract)}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/7211}, DOI={10.1609/aaai.v34i10.7211}, abstractNote={<p>We demonstrate that projecting data points into hyperplanes is good strategy for general-purpose kernel design. We used three different hyperplanes generation schemes, random, convex hull and α-shape, and evaluated the results on two synthetic and three well known image-based datasets. The results showed considerable improvement in the classification performance in almost all scenarios, corroborating the claim that such an approach can be used as a general-purpose kernel transformation. Also, we discuss some connection with Convolutional Neural Networks and how such an approach could be used to understand such networks better.</p>}, number={10}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Moreira Cestari, Daniel and Mello, Rodrigo Fernandes de}, year={2020}, month={Apr.}, pages={13877-13878} }