@inproceedings{
    Peltonen09icassp,
    author = "Peltonen, Jaakko and Aidos, Helena and Kaski, Samuel",
    publisher = "IEEE",
    title = "Supervised Nonlinear Dimensionality Reduction by Neighbor Retrieval",
    url = "http://dx.doi.org/10.1109/ICASSP.2009.4959957",
    booktitle = "Proceedings of ICASSP 2009, the IEEE International Conference on Acoustics, Speech, and Signal Processing",
    corerank = "B",
    abstract = "Many recent works have combined two machine learning topics, learning of supervised distance metrics and manifold embedding methods, into supervised nonlinear dimensionality reduction methods. We show that a combination of an early metric learning method and a recent unsupervised dimensionality reduction method empirically outperforms previous methods. In our method, the Riemannian distance metric measures local change of class distributions, and the dimensionality reduction method makes a rigorous tradeoff between precision and recall in retrieving similar data points based on the reduced-dimensional display. The resulting supervised visualizations are good for finding (sets of) similar data samples that have similar class distributions.",
    responsibleauthor = "Kaski, Samuel",
    flags = "AIRC HIIT public copy",
    year = "2009",
    impactfactor = "D3",
    pages = "1809-1812"
}