@inproceedings{5394d24d3ca944e89bb41366818b83ea,
title = "Soft rank clustering",
abstract = "Clustering methods provide an useful tool to tackle the problem of exploring large-dimensional data. However many common approaches suffer from being applied in high-dimensional spaces. Building on a dissimilarity-based representation of data, we propose a dimensionality reduction technique which preserves the clustering structure of the data. The technique is designed for cases in which data dimensionality is large compared to the number of available observations. In these cases, we represent data in the space of soft D-ranks, by applying the concept of fuzzy ranking. A clustering procedure is then applied. Experimental results show that the method is able to retain the necessary information, while considerably reducing dimensionality.",
author = "Stefano Rovetta and Francesco Masulli and Maurizio Filippone",
year = "2006",
doi = "10.1007/11731177_29",
language = "English (US)",
isbn = "3540331832",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "207--213",
booktitle = "Neural Nets - 16th Italian Workshop on Neural Nets, WIRN 2005, and International Workshop on Natural and Artificial Immune Systems, NAIS 2005, Revised Selected Papers",
note = "16th Italian Workshop on Neural Nets, WIRN 2005, and International Workshop on Natural and Artificial Immune Systems, NAIS 2005 ; Conference date: 08-06-2005 Through 11-06-2005",
}