Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Psarros, Ioannis"'
We present algorithms for the computation of $\varepsilon$-coresets for $k$-median clustering of point sequences in $\mathbb{R}^d$ under the $p$-dynamic time warping (DTW) distance. Coresets under DTW have not been investigated before, and the analys
Externí odkaz:
http://arxiv.org/abs/2312.09838
Autor:
Kalimeris, Alexandros, Psarros, Ioannis, Giannopoulos, Giorgos, Terrovitis, Manolis, Papastefanatos, George, Kotsis, Gregory
Soiling is the accumulation of dirt in solar panels which leads to a decreasing trend in solar energy yield and may be the cause of vast revenue losses. The effect of soiling can be reduced by washing the panels, which is, however, a procedure of non
Externí odkaz:
http://arxiv.org/abs/2301.12939
Autor:
Psarros, Ioannis, Rohde, Dennis
Modern time series analysis requires the ability to handle datasets that are inherently high-dimensional; examples include applications in climatology, where measurements from numerous sensors must be taken into account, or inventory tracking of larg
Externí odkaz:
http://arxiv.org/abs/2207.07442
We study variants of the mean problem under the $p$-Dynamic Time Warping ($p$-DTW) distance, a popular and robust distance measure for sequential data. In our setting we are given a set of finite point sequences over an arbitrary metric space and we
Externí odkaz:
http://arxiv.org/abs/2112.00408
We study the $c$-approximate near neighbor problem under the continuous Fr\'echet distance: Given a set of $n$ polygonal curves with $m$ vertices, a radius $\delta > 0$, and a parameter $k \leq m$, we want to preprocess the curves into a data structu
Externí odkaz:
http://arxiv.org/abs/2107.07792
Autor:
Driemel, Anne, Psarros, Ioannis
We study approximate-near-neighbor data structures for time series under the continuous Fr\'echet distance. For an attainable approximation factor $c>1$ and a query radius $r$, an approximate-near-neighbor data structure can be used to preprocess $n$
Externí odkaz:
http://arxiv.org/abs/2008.09406
We study metric data structures for curves in doubling spaces, such as trajectories of moving objects in Euclidean $\mathbb{R}^d$, where the distance between two curves is measured using the discrete Fr\'echet distance. We design data structures in a
Externí odkaz:
http://arxiv.org/abs/1907.04420
The Vapnik-Chervonenkis dimension provides a notion of complexity for systems of sets. If the VC dimension is small, then knowing this can drastically simplify fundamental computational tasks such as classification, range counting, and density estima
Externí odkaz:
http://arxiv.org/abs/1903.03211
Randomized dimensionality reduction has been recognized as one of the fundamental techniques in handling high-dimensional data. Starting with the celebrated Johnson-Lindenstrauss Lemma, such reductions have been studied in depth for the Euclidean $(\
Externí odkaz:
http://arxiv.org/abs/1902.08815
Publikováno v:
In Theoretical Computer Science 9 January 2023 942:169-179
