@inproceedings{
    Peltonen03wsom,
    author = "Peltonen, Jaakko and Klami, Arto and Kaski, Samuel",
    publisher = "Kyushu Institute of Technology",
    title = "Learning Metrics for Information Visualization",
    url = "http://www.cis.hut.fi/projects/mi/papers/wsom03b.ps.gz",
    booktitle = "Proceedings of WSOM'03, Workshop on Self-Organizing Maps",
    year = "2003",
    abstract = "The learning metrics principle shows how (nonlinear) projection and clustering methods can be made to focus on discriminative properties of data. In this paper we review and extend our earlier work on learning metrics for self-organizing maps (SOMs), compare algorithms, and introduce a new accurate distance computation algorithm. It can be used with methods that work on pairwise distances between the data samples. Its usefulness is demonstrated for Sammon's mapping, a form of multidimensional scaling.",
    note = "(Proceedings on CD-ROM)",
    flags = "AIRC copy",
    address = "Kitakyushy, Japan",
    impactfactor = "D3",
    pages = "213-218"
}