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Differential privacy is a rigorous definition for privacy that guarantees that any analysis performed on a sensitive dataset leaks no information about the individuals whose data are contained therein. In this work, we develop new differentially priv
Externí odkaz:
http://arxiv.org/abs/2111.08784
Autor:
Mandourarakis, Ioannis, Gogolou, Vasiliki, Agorastou, Zoi, Voutsinas, Stylianos, Rigogiannis, Nick, Koutroulis, Eftichios, Siskos, Stylianos, Samarakou, Maria, Papanikolaou, Nick, Karystinos, George
Publikováno v:
In Energy Conversion and Management 1 October 2023 293
Autor:
Doundoulakis, Ioannis, Gatzoulis, Konstantinos A., Arsenos, Petros, Dilaveris, Polychronis, Tsiachris, Dimitris, Antoniou, Christos-Konstantinos, Sideris, Skevos, Kordalis, Athanasios, Soulaidopoulos, Stergios, Karystinos, George, Pylarinou, Voula, Archontakis, Stefanos, Laina, Ageliki, Gialernios, Theodoros, Xydis, Panagiotis, Sotiropoulos, Ilias, Vlachopoulos, Charalambos, Tsioufis, Konstantinos
Publikováno v:
In Hellenic Journal of Cardiology March-April 2022 64:24-29
We describe ways to define and calculate $L_1$-norm signal subspaces which are less sensitive to outlying data than $L_2$-calculated subspaces. We start with the computation of the $L_1$ maximum-projection principal component of a data matrix contain
Externí odkaz:
http://arxiv.org/abs/1405.6785
Certain optimization problems in communication systems, such as limited-feedback constant-envelope beamforming or noncoherent $M$-ary phase-shift keying ($M$PSK) sequence detection, result in the maximization of a fixed-rank positive semidefinite qua
Externí odkaz:
http://arxiv.org/abs/1401.6968
The computation of the sparse principal component of a matrix is equivalent to the identification of its principal submatrix with the largest maximum eigenvalue. Finding this optimal submatrix is what renders the problem ${\mathcal{NP}}$-hard. In thi
Externí odkaz:
http://arxiv.org/abs/1312.5891
We describe ways to define and calculate $L_1$-norm signal subspaces which are less sensitive to outlying data than $L_2$-calculated subspaces. We focus on the computation of the $L_1$ maximum-projection principal component of a data matrix containin
Externí odkaz:
http://arxiv.org/abs/1309.1194
We consider the problem of identifying the sparse principal component of a rank-deficient matrix. We introduce auxiliary spherical variables and prove that there exists a set of candidate index-sets (that is, sets of indices to the nonzero elements o
Externí odkaz:
http://arxiv.org/abs/1106.1651
Akademický článek
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Differential privacy is a rigorous definition for privacy that guarantees that any analysis performed on a sensitive dataset leaks no information about the individuals whose data are contained therein. In this work, we develop new differentially priv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f152722d9a97c8e67bee8e4fd54c2dcd
http://arxiv.org/abs/2111.08784
http://arxiv.org/abs/2111.08784