Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Puoya Tabaghi"'
Publikováno v:
Biology, Vol 11, Iss 9, p 1256 (2022)
Phylogenetic placement, used widely in ecological analyses, seeks to add a new species to an existing tree. A deep learning approach was previously proposed to estimate the distance between query and backbone species by building a map from gene seque
Externí odkaz:
https://doaj.org/article/32fa79f253094ba688157198c141f5c7
Autor:
Ivan Dokmanic, Puoya Tabaghi
Publikováno v:
IEEE Signal Processing Letters. 28:1120-1124
Congruent Procrustes analysis aims to find the best matching between two point sets through rotation, reflection and translation. We formulate the Procrustes problem for hyperbolic spaces, review the canonical definition of the center mass for a poin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b37454daee4b94493c4bd573ca30e16c
https://doi.org/10.1109/lsp.2021.3081379
https://doi.org/10.1109/lsp.2021.3081379
Publikováno v:
IEEE Transactions on Signal Processing. 68:452-465
Euclidean distance matrices (EDMs) are a major tool for localization from distances, with applications ranging from protein structure determination to global positioning and manifold learning. They are, however, static objects which serve to localize
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6dd065369eb0ae4c3dde9f477ab4ce31
https://doi.org/10.1109/tsp.2019.2959260
https://doi.org/10.1109/tsp.2019.2959260
Many high-dimensional practical data sets have hierarchical structures induced by graphs or time series. Such data sets are hard to process in Euclidean spaces and one often seeks low-dimensional embeddings in other space forms to perform the require
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::45335f6fc88052a5e3cffd1b2190eea6
http://arxiv.org/abs/2203.03730
http://arxiv.org/abs/2203.03730
Publikováno v:
Comparative Genomics ISBN: 9783031062193
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::886e32fefeea7706be29fe1093c21657
https://doi.org/10.1007/978-3-031-06220-9_5
https://doi.org/10.1007/978-3-031-06220-9_5
Autor:
Puoya Tabaghi, Ivan Dokmanic
Publikováno v:
KDD
Hyperbolic space is a natural setting for mining and visualizing data with hierarchical structure. In order to compute a hyperbolic embedding from comparison or similarity information, one has to solve a hyperbolic distance geometry problem. In this
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a8c8486a1589887ed31e5ee76b1f8e16
https://doi.org/10.1145/3394486.3403224
https://doi.org/10.1145/3394486.3403224
Publikováno v:
ICASSP
In this paper, we propose kinetic Euclidean distance matrices (KEDMs) a new algebraic tool for localization of moving points from spatio temporal distance measurements. KEDMs are inspired by the well-known Euclidean distance matrices (EDM) which mode
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ca239ddbcecc84068ccfb3fbfeb22f87
https://doi.org/10.1109/icassp.2019.8682653
https://doi.org/10.1109/icassp.2019.8682653
Publikováno v:
IJCNN
This paper proposes a supervised approach for analysis of high-dimensional data using low-dimensional submanifolds. This method offers many useful properties. Using first order approximation for the given nonlinear mapping, we introduce a locally lin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::248317736c6f2cd39148e9606db7889e
https://doi.org/10.1109/ijcnn.2015.7280821
https://doi.org/10.1109/ijcnn.2015.7280821