@inproceedings{161d5d1fa37f46c080cb4e8ba651f947,
title = "ELMVIS+: Improved Nonlinear Visualization Technique Using Cosine Distance and Extreme Learning Machines",
abstract = "This paper presents ELMVIS+, a significant improvement in ELMVIS methodology that enables faster computation, more stable results and a wider application range. The novel cost function and a fast way of estimating it speeds up the method compared to ELMVIS, especially in large-dimensional datasets. The included Genetic Algorithms add global optimization that helps ELMVIS+ to find a better optimum. The improved methodology shows state-of-the-art performance in three different benchmark datasets.",
keywords = "512 Business and Management, Visualization, Nonlinear dimensionality reduction, Machine learning, Neural network, Genetic algorithms, Cosine distance, Extreme learning machines, Big data, Big dimensionality, Projection",
author = "Anton Akusok and Yoan Miche and Kaj-Mikael Bj{\"o}rk and Rui Nian and Paula Lauren and Amaury Lendasse",
year = "2016",
month = jan,
day = "3",
doi = "10.1007/978-3-319-28373-9_31",
language = "English",
isbn = "978-3-319-28372-2",
volume = "2",
series = " Proceedings in Adaptation, Learning and Optimization (PALO)",
publisher = "Springer",
pages = "357--369 ",
booktitle = "Proceedings of ELM-2015",
address = "International",
}