@article{
    Akusok2013a,
    author = "Akusok, Anton and Miche, Yoan and Lendasse, Amaury and Nian, Rui",
    responsibleauthor = "Anton, Akusok and Miche, Yoan",
    language = "eng",
    title = "ELMVIS: a Nonlinear Visualization Technique using Random Permutations and Extreme Learning Machine",
    url = "",
    country = "United States",
    abstract = "A nonlinear data visualization method based on extreme learning machine (ELM) is proposed in this paper. The novel idea is to pair original data points with pre-selected visualization ones, using random permutations and the adjusted ELM as an extremely fast nonlinear error metric. Despite the fact that the exact solution with permutation has a factorial complexity, experiments show a fast convergence rate for small to medium sample sets. The method is tested on three benchmark datasets, namely a spiral, artificial and real face images datasets. The proposed methodology succeeded in all of them, showing performance comparable or above the current state-of-the-art visualization methods.",
    issn = "NA",
    number = "NA",
    pages = "NA",
    volume = "NA",
    juforank = "2",
    flags = "AIRC",
    il = "yes",
    keywords = "ELM, random permutation, visualization",
    year = "to appear",
    date = "2013-08-05",
    unitcode = "T306-100",
    kay = "NA",
    impactfactor = "A1",
    journal = "IEEE Intelligent Systems"
}