-
1
-
-
79952446596
-
Scalable tensor factorizations for incomplete data
-
E. Acar, D. M. Dunlavy, T. G. Kolda, and M. Mørup. Scalable tensor factorizations for incomplete data. Chemometrics and Intelligent Laboratory Systems, 106(1):41-56, 2011.
-
(2011)
Chemometrics and Intelligent Laboratory Systems
, vol.106
, Issue.1
, pp. 41-56
-
-
Acar, E.1
Dunlavy, D.M.2
Kolda, T.G.3
Mørup, M.4
-
3
-
-
84946062590
-
Flexifact: Scalable flexible factorization of coupled tensors on hadoop
-
A. Beutel, A. Kumar, E. E. Papalexakis, P. P. Talukdar, C. Faloutsos, and E. P. Xing. Flexifact: Scalable flexible factorization of coupled tensors on hadoop. In SDM, 2014.
-
(2014)
SDM
-
-
Beutel, A.1
Kumar, A.2
Papalexakis, E.E.3
Talukdar, P.P.4
Faloutsos, C.5
Xing, E.P.6
-
4
-
-
80051762104
-
Distributed optimization and statistical learning via the alternating direction method of multipliers
-
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3(1):1-122, 2011.
-
(2011)
Foundations and Trends in Machine Learning
, vol.3
, Issue.1
, pp. 1-122
-
-
Boyd, S.1
Parikh, N.2
Chu, E.3
Peleato, B.4
Eckstein, J.5
-
5
-
-
34250252877
-
Candelinc: A general approach to multidimensional analysis of many-way arrays with linear constraints on parameters
-
J. D. Carroll, S. Pruzansky, and J. B. Kruskal. Candelinc: A general approach to multidimensional analysis of many-way arrays with linear constraints on parameters. Psychometrika, 45(1):3-24, 1980.
-
(1980)
Psychometrika
, vol.45
, Issue.1
, pp. 3-24
-
-
Carroll, J.D.1
Pruzansky, S.2
Kruskal, J.B.3
-
8
-
-
84888200992
-
Applying active learning to high-throughput phenotyping algorithms for electronic health records data
-
Y. Chen et al. Applying active learning to high-throughput phenotyping algorithms for electronic health records data. JAMIA, 2013.
-
(2013)
JAMIA
-
-
Chen, Y.1
-
9
-
-
84871551012
-
On tensors, sparsity, and nonnegative factorizations
-
E. C. Chi and T. G. Kolda. On tensors, sparsity, and nonnegative factorizations. SIAM Journal on Matrix Analysis and Applications, 33(4):1272-1299, 2012.
-
(2012)
SIAM Journal on Matrix Analysis and Applications
, vol.33
, Issue.4
, pp. 1272-1299
-
-
Chi, E.C.1
Kolda, T.G.2
-
10
-
-
85018832771
-
Network discovery via constrained tensor analysis of fmri data
-
I. Davidson, S. Gilpin, O. Carmichael, and P. Walker. Network discovery via constrained tensor analysis of fmri data. In KDD, 2013.
-
(2013)
KDD
-
-
Davidson, I.1
Gilpin, S.2
Carmichael, O.3
Walker, P.4
-
11
-
-
77952822074
-
Phewas: Demonstrating the feasibility of a phenome-wide scan to discover gene disease associations
-
J. C. Denny et al. Phewas: demonstrating the feasibility of a phenome-wide scan to discover gene disease associations. Bioinformatics, 26(9):1205-1210, 2010.
-
(2010)
Bioinformatics
, vol.26
, Issue.9
, pp. 1205-1210
-
-
Denny, J.C.1
-
12
-
-
84890107642
-
Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data
-
J. C. Denny et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nature Biotechnology, 31(12):1102-1111, 2013.
-
(2013)
Nature Biotechnology
, vol.31
, Issue.12
, pp. 1102-1111
-
-
Denny, J.C.1
-
13
-
-
84921067095
-
Link prediction in heterogeneous data via generalized coupled tensor factorization
-
B. Ermiş, E. Acar, and A. T. Cemgil. Link prediction in heterogeneous data via generalized coupled tensor factorization. Data Mining and Knowledge Discovery, 29(1):203-236, 2015.
-
(2015)
Data Mining and Knowledge Discovery
, vol.29
, Issue.1
, pp. 203-236
-
-
Ermiş, B.1
Acar, E.2
Cemgil, A.T.3
-
14
-
-
84919839072
-
Limestone: High-throughput candidate phenotype generation via tensor factorization
-
J. C. Ho, J. Ghosh, S. R. Steinhubl, W. F. Stewart, J. C. Denny, B. A. Malin, and J. Sun. Limestone: High-throughput candidate phenotype generation via tensor factorization. Journal of biomedical informatics, 52:199-211, 2014.
-
(2014)
Journal of Biomedical Informatics
, vol.52
, pp. 199-211
-
-
Ho, J.C.1
Ghosh, J.2
Steinhubl, S.R.3
Stewart, W.F.4
Denny, J.C.5
Malin, B.A.6
Sun, J.7
-
15
-
-
84907024756
-
Marble: High-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization
-
J. C. Ho, J. Ghosh, and J. Sun. Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization. In KDD, 2014.
-
(2014)
KDD
-
-
Ho, J.C.1
Ghosh, J.2
Sun, J.3
-
17
-
-
79955035027
-
Electronic medical records for genetic research: Results of the emerge consortium
-
A. N. Kho et al. Electronic medical records for genetic research: Results of the emerge consortium. Science Translational Medicine, 3(79):79re1, 2011.
-
(2011)
Science Translational Medicine
, vol.3
, Issue.79
-
-
Kho, A.N.1
-
18
-
-
84949178333
-
Fast nonnegative tensor factorization with an active-set-like method
-
Springer
-
J. Kim and H. Park. Fast nonnegative tensor factorization with an active-set-like method. In High-Performance Scientific Computing, pages 311-326. Springer, 2012.
-
(2012)
High-Performance Scientific Computing
, pp. 311-326
-
-
Kim, J.1
Park, H.2
-
19
-
-
68649096448
-
Tensor decompositions and applications
-
T. G. Kolda and B. W. Bader. Tensor decompositions and applications. SIAM review, 51(3):455-500, 2009.
-
(2009)
SIAM Review
, vol.51
, Issue.3
, pp. 455-500
-
-
Kolda, T.G.1
Bader, B.W.2
-
20
-
-
84902838492
-
Low-rank tensor completion by riemannian optimization
-
D. Kressner, M. Steinlechner, and B. Vandereycken. Low-rank tensor completion by riemannian optimization. BIT Numerical Mathematics, 54(2):447-468, 2014.
-
(2014)
BIT Numerical Mathematics
, vol.54
, Issue.2
, pp. 447-468
-
-
Kressner, D.1
Steinlechner, M.2
Vandereycken, B.3
-
21
-
-
85162350693
-
Linearized alternating direction method with adaptive penalty for low-rank representation
-
Z. Lin, R. Liu, and Z. Su. Linearized alternating direction method with adaptive penalty for low-rank representation. In NIPS, 2011.
-
(2011)
NIPS
-
-
Lin, Z.1
Liu, R.2
Su, Z.3
-
22
-
-
84870175618
-
Tensor completion for estimating missing values in visual data
-
J. Liu, P. Musialski, P. Wonka, and J. Ye. Tensor completion for estimating missing values in visual data. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(1):208-220, 2013.
-
(2013)
Pattern Analysis and Machine Intelligence, IEEE Transactions on
, vol.35
, Issue.1
, pp. 208-220
-
-
Liu, J.1
Musialski, P.2
Wonka, P.3
Ye, J.4
-
23
-
-
84959917731
-
Factor matrix trace norm minimization for low-rank tensor completion
-
Y. Liu, F. Shang, H. Cheng, J. Cheng, and H. Tong. Factor matrix trace norm minimization for low-rank tensor completion. In SDM, 2014.
-
(2014)
SDM
-
-
Liu, Y.1
Shang, F.2
Cheng, H.3
Cheng, J.4
Tong, H.5
-
24
-
-
84919948669
-
Square deal: Lower bounds and improved relaxations for tensor recovery
-
C. Mu, B. Huang, J. Wright, and D. Goldfarb. Square deal: Lower bounds and improved relaxations for tensor recovery. In ICML, 2013.
-
(2013)
ICML
-
-
Mu, C.1
Huang, B.2
Wright, J.3
Goldfarb, D.4
-
25
-
-
84864537879
-
Tensor factorization using auxiliary information
-
A. Narita, K. Hayashi, R. Tomioka, and H. Kashima. Tensor factorization using auxiliary information. Data Mining and Knowledge Discovery, 25(2):298-324, 2012.
-
(2012)
Data Mining and Knowledge Discovery
, vol.25
, Issue.2
, pp. 298-324
-
-
Narita, A.1
Hayashi, K.2
Tomioka, R.3
Kashima, H.4
-
26
-
-
84925999415
-
Turbo-SMT: Accelerating coupled sparse matrix-tensor factorizations by 200x
-
E. E. Papalexakis, T. M. Mitchell, N. D. Sidiropoulos, C. Faloutsos, P. P. Talukdar, and B. Murphy. Turbo-smt: Accelerating coupled sparse matrix-tensor factorizations by 200x. In SDM, 2014.
-
(2014)
SDM
-
-
Papalexakis, E.E.1
Mitchell, T.M.2
Sidiropoulos, N.D.3
Faloutsos, C.4
Talukdar, P.P.5
Murphy, B.6
-
27
-
-
84890479944
-
Electronic health records-driven phenotyping: Challenges, recent advances, and perspectives
-
J. Pathak, A. N. Kho, and J. Denny. Electronic health records-driven phenotyping: challenges, recent advances, and perspectives. Journal of the American Medical Informatics Association, 20(e2):e206-e211, 2013.
-
(2013)
Journal of the American Medical Informatics Association
, vol.20
, Issue.E2
, pp. e206-e211
-
-
Pathak, J.1
Kho, A.N.2
Denny, J.3
-
28
-
-
84898929494
-
A new convex relaxation for tensor completion
-
B. Romera-Paredes and M. Pontil. A new convex relaxation for tensor completion. In NIPS, 2013.
-
(2013)
NIPS
-
-
Romera-Paredes, B.1
Pontil, M.2
-
29
-
-
84894616525
-
Learning with tensors: A framework based on convex optimization and spectral regularization
-
M. Signoretto, Q. T. Dinh, L. De Lathauwer, and J. A. Suykens. Learning with tensors: a framework based on convex optimization and spectral regularization. Machine Learning, 94(3):303-351, 2014.
-
(2014)
Machine Learning
, vol.94
, Issue.3
, pp. 303-351
-
-
Signoretto, M.1
Dinh, Q.T.2
De Lathauwer, L.3
Suykens, J.A.4
-
31
-
-
84899031700
-
Convex tensor decomposition via structured schatten norm regularization
-
R. Tomioka and T. Suzuki. Convex tensor decomposition via structured schatten norm regularization. In NIPS, 2013.
-
(2013)
NIPS
-
-
Tomioka, R.1
Suzuki, T.2
-
33
-
-
84885030988
-
A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion
-
Y. Xu and W. Yin. A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion. SIAM Journal on Imaging Sciences, 6(3):1758-1789, 2013.
-
(2013)
SIAM Journal on Imaging Sciences
, vol.6
, Issue.3
, pp. 1758-1789
-
-
Xu, Y.1
Yin, W.2
|