Zobrazeno 1 - 5
of 5
pro vyhledávání: '"John T. Holodnak"'
Publikováno v:
2022 IEEE International Conference on Data Mining Workshops (ICDMW).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dfdd1c863fa401b0d3c03e8ad7b8b1de
https://doi.org/10.1109/icdmw58026.2022.00080
https://doi.org/10.1109/icdmw58026.2022.00080
Publikováno v:
SIAM Journal on Matrix Analysis and Applications. 39:1208-1220
Given a real symmetric positive semi-definite matrix E, and an approximation S that is a sum of n independent matrix-valued random variables, we present bounds on the relative error in S due to randomization. The bounds do not depend on the matrix di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d89c77292e7cbf8621592823ef00767e
https://doi.org/10.1137/17m1141503
https://doi.org/10.1137/17m1141503
Autor:
Ilse C. F. Ipsen, John T. Holodnak
Publikováno v:
SIAM Journal on Matrix Analysis and Applications. 36:110-137
Given a real matrix A with n columns, the problem is to approximate the Gram product AA^T by c << n weighted outer products of columns of A. Necessary and sufficient conditions for the exact computation of AA^T (in exact arithmetic) from c >= rank(A)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dd2f08fc1f0625706bdc2e8ab9e74c40
https://doi.org/10.1137/130940116
https://doi.org/10.1137/130940116
Autor:
Shannon C. Roberts, Sophia Yuditskaya, Trang Nguyen, William W. Streilein, Maja Milosavljevic, John T. Holodnak
Publikováno v:
IEEE Symposium on Security and Privacy Workshops
Recent high profile security breaches have highlighted the importance of insider threat detection systems for cybersecurity. However, issues such as high false positive rates and concerns over data privacy make it difficult to predict performance wit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d39fe389fbbfcbc1f80ff788d154cfbf
https://doi.org/10.1109/spw.2016.14
https://doi.org/10.1109/spw.2016.14
The leverage scores of a full-column rank matrix A are the squared row norms of any orthonormal basis for range(A). We show that corresponding leverage scores of two matrices A and A + \Delta A are close in the relative sense, if they have large magn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6512d5dfdfe0f2fc55457ebc268b1644
http://arxiv.org/abs/1402.0957
http://arxiv.org/abs/1402.0957
