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pro vyhledávání: '"Thordsen, Erik"'
Autor:
Thordsen, Erik, Schubert, Erich
Many algorithms require discriminative boundaries, such as separating hyperplanes or hyperballs, or are specifically designed to work on spherical data. By applying inversive geometry, we show that the two discriminative boundaries can be used interc
Externí odkaz:
http://arxiv.org/abs/2405.18401
Autor:
Thordsen, Erik, Schubert, Erich
The merit of projecting data onto linear subspaces is well known from, e.g., dimension reduction. One key aspect of subspace projections, the maximum preservation of variance (principal component analysis), has been thoroughly researched and the effe
Externí odkaz:
http://arxiv.org/abs/2209.12485
Autor:
Thordsen, Erik, Schubert, Erich
Many approaches in the field of machine learning and data analysis rely on the assumption that the observed data lies on lower-dimensional manifolds. This assumption has been verified empirically for many real data sets. To make use of this manifold
Externí odkaz:
http://arxiv.org/abs/2107.06566
Autor:
Thordsen, Erik, Schubert, Erich
The intrinsic dimensionality refers to the ``true'' dimensionality of the data, as opposed to the dimensionality of the data representation. For example, when attributes are highly correlated, the intrinsic dimensionality can be much lower than the n
Externí odkaz:
http://arxiv.org/abs/2006.12880
Autor:
Brockmann, Henning, Goclik, Jan, Günter, Christian, Harder, Sergei, Heming, Tim, Humann, Sebastian, Ihne, Michael, Mühlig, Jan, Ostwald, Tibor, Thordsen, Erik, Threbank, Sascha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7675ff0140e1e5b09f870cf39e7c5871