Nowadays, the increasing amount of information provided by hyperspectral sensors requires optimal solutions to ease the subsequent analysis of the produced data. A common issue in this matter relates to the hyperspectral data representation for classification tasks. Existing approaches address the data representation problem by performing a dimensionality reduction over the original data. However, mining complementary features that reduce the redundancy from the multiple levels of hyperspectral images remains challenging. Thus, exploiting the representation power of neural networks based techniques becomes an attractive alternative in this matter. In this work, we propose a novel dimensionality reduction implementation for hyperspectral imaging based on autoencoders, ensuring the orthogonality among features to reduce the redundancy in hyperspectral data. The experiments conducted on the...

La visión por computador es un área de estudio en la inteligencia artificial que se enfoca en el desarrollo de técnicas computacionales para percibir el mundo a través de entradas visuales, como videos o imágenes. El aprendizaje profundo ha demostrado ser una técnica eficiente para el análisis e interpretación de datos visuales. Sin embargo, afronta innumerables desafíos según su aplicación en las diferentes tareas de la visión por computador. Este panel reúne un grupo de expertos en aprendizaje profundo, quienes ofrecerán información sobre su aplicación y los desafíos en sus respectivas áreas de investigación con relación a la visión por computador.

Cyberbullying is a social problem in which bullies’ actions are more harmful than in traditional forms of bullying as they have the power to repeatedly humiliate the victim in front of an entire community through social media. Nowadays, multiple works aim at detecting acts of cyberbullying via the analysis of texts in social media publications written in one or more languages; however, few investigations target the cyberbullying detection in the Spanish language. In this work, we aim to compare four traditional supervised machine learning methods performances in detecting cyberbullying via the identification of four cyberbullying-related categories on Twitter posts written in the Peruvian Spanish language. Specifically, we trained and tested the Naive Bayes, Multinomial Logistic Regression, Support Vector Machines, and Random Forest classifiers upon a manually annotated dataset with th...