Zobrazeno 1 - 10
of 90
pro vyhledávání: '"Eigengap"'
Autor:
Meiby Ortiz-Bouza, Selin Aviyente
Publikováno v:
IEEE Access, Vol 12, Pp 6423-6436 (2024)
Networks are commonly used to model complex systems. The different entities in the system are represented by nodes of the network and their interactions by edges. In most real life systems, the different entities may interact in different ways necess
Externí odkaz:
https://doaj.org/article/d4b5c322f2ed47139165fa9f55ac6d6b
Publikováno v:
IEEE Access, Vol 8, Pp 75035-75042 (2020)
The development of civil aviation has led to flight operations generating massive datasets. Derived from Automatic Dependent Surveillance-Broadcast technology, an eigengap-based automatic hierarchical clustering algorithm is proposed, aiming to overc
Externí odkaz:
https://doaj.org/article/b1aa41b247ef4463bb244bb3b2ce2bfd
Publikováno v:
International Journal of Applied Mathematics and Computer Science, Vol 28, Iss 4, Pp 771-786 (2018)
The paper presents a novel spectral algorithm EVSA (eigenvector structure analysis), which uses eigenvalues and eigenvectors of the adjacency matrix in order to discover clusters. Based on matrix perturbation theory and properties of graph spectra we
Externí odkaz:
https://doaj.org/article/b41763064aa74293b6c45056446fad5f
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Digital Communications and Networks, Vol 7, Iss 1, Pp 157-166 (2021)
Most of the intelligent surveillances in the industry only care about the safety of the workers. It is meaningful if the camera can know what, where and how the worker has performed the action in real time. In this paper, we propose a light-weight an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b74a1f23cd45807a1268ee0f0f614ffc
https://doi.org/10.1016/j.dcan.2020.05.004
https://doi.org/10.1016/j.dcan.2020.05.004
Meta-Learning With Latent Space Clustering in Generative Adversarial Network for Speaker Diarization
Autor:
Somer L. Bishop, Manoj Kumar, Catherine Lord, Shrikanth S. Narayanan, Tae Jin Park, Monisankha Pal, So Hyun Kim, Raghuveer Peri
Publikováno v:
IEEE/ACM Trans Audio Speech Lang Process
The performance of most speaker diarization systems with x-vector embeddings is both vulnerable to noisy environments and lacks domain robustness. Earlier work on speaker diarization using generative adversarial network (GAN) with an encoder network
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b725b5e574652addf4d59cd32f2e6329
https://doi.org/10.1109/taslp.2021.3061885
https://doi.org/10.1109/taslp.2021.3061885
Publikováno v:
Computer Science and Application. 10:289-302
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f5f20dbeb0591e13a11dbfa7d2d1c548
https://doi.org/10.12677/csa.2020.102030
https://doi.org/10.12677/csa.2020.102030
Autor:
Milad Afzalan, Farrokh Jazizadeh
Publikováno v:
Neurocomputing. 347:94-108
Spectral clustering algorithms typically require a priori selection of input parameters such as the number of clusters, a scaling parameter for the affinity measure, or ranges of these values for parameter tuning. Despite efforts for automating the p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a25966cf3e9bb0e1671a4ff1f965333c
https://doi.org/10.1016/j.neucom.2019.03.008
https://doi.org/10.1016/j.neucom.2019.03.008
Publikováno v:
Neurocomputing. 332:129-136
It has been shown that the first two extremal eigenpairs of symmetric matrices are involved a lot in spectral clustering, dimensionality reduction, image segmentation, and graph theory. We also know that the eigengap directly affects the stability of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::57ec8df9539bf29da2870b02b00d503b
https://doi.org/10.1016/j.neucom.2018.12.042
https://doi.org/10.1016/j.neucom.2018.12.042
Publikováno v:
International Journal of Applied Mathematics and Computer Science, Vol 28, Iss 4, Pp 771-786 (2018)
The paper presents a novel spectral algorithm EVSA (eigenvector structure analysis), which uses eigenvalues and eigenvectors of the adjacency matrix in order to discover clusters. Based on matrix perturbation theory and properties of graph spectra we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::858bfdff8b774081cbc926184e0a43eb
https://doaj.org/article/b41763064aa74293b6c45056446fad5f
https://doaj.org/article/b41763064aa74293b6c45056446fad5f