Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Nicholas Arcolano"'
Autor:
Nicholas Arcolano, Richard Vaz
Publikováno v:
2001 Annual Conference Proceedings.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ea99d5d2b1cb8fc4836ac4362e0a5f92
https://doi.org/10.18260/1-2--9876
https://doi.org/10.18260/1-2--9876
Autor:
Nicholas Arcolano, Benjamin A. Miller
Publikováno v:
ICASSP
Recent work on signal detection in graph-based data focuses on classical detection when the signal and noise are both in the form of discrete entities and their relationships. In practice, the relationships of interest may not be directly observable,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ac43794b10029aff0fec786771ce7eb
https://doi.org/10.1109/icassp.2014.6854236
https://doi.org/10.1109/icassp.2014.6854236
Publikováno v:
ISI
When working with large-scale network data, the interconnected entities often have additional descriptive information. This additional metadata may provide insight that can be exploited for detection of anomalous events. In this paper, we use a gener
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a51d78d75e0c3a8943de174a5d202a5c
https://doi.org/10.1109/isi.2013.6578815
https://doi.org/10.1109/isi.2013.6578815
Publikováno v:
ICASSP
As abstract representations of relational data, graphs and networks find wide use in a variety of fields, particularly when working in non-Euclidean spaces. Yet for graphs to be truly useful in in the context of signal processing, one ultimately must
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fb81901b60b91a5d4e486ca0312c3f0e
https://doi.org/10.1109/icassp.2012.6288785
https://doi.org/10.1109/icassp.2012.6288785
Autor:
Nadya T. Bliss, Michelle S. Beard, Nicholas Arcolano, Benjamin A. Miller, Jeremy Kepner, Patrick J. Wolfe, Matthew C. Schmidt
Publikováno v:
ICASSP
In many applications, it is convenient to represent data as a graph, and often these datasets will be quite large. This paper presents an architecture for analyzing massive graphs, with a focus on signal processing applications such as modeling, filt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6583cca3197f07c5721b76fe0b8bc8c9
https://doi.org/10.1109/icassp.2012.6289124
https://doi.org/10.1109/icassp.2012.6289124
Autor:
Patrick J. Wolfe, Nicholas Arcolano
Publikováno v:
ICASSP
Covariance matrix estimates are an essential part of many signal processing algorithms, and are often used to determine a low-dimensional principal subspace via their spectral decomposition. However, for sufficiently high-dimensional matrices exact e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bed8dd16f8e13609ff92f5afaee52d39
https://doi.org/10.1109/icassp.2011.5947175
https://doi.org/10.1109/icassp.2011.5947175
Autor:
Patrick J. Wolfe, Nicholas Arcolano
Publikováno v:
ICASSP
Spectral methods requiring the computation of eigenvalues and eigenvectors of a positive definite matrix are an essential part of signal processing. However, for sufficiently high-dimensional data sets, the eigenvalue problem cannot be solved without
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::907765ccf5962db6f79a6ffe5adfec25
https://doi.org/10.1109/icassp.2010.5495906
https://doi.org/10.1109/icassp.2010.5495906