-
1
-
-
84880258085
-
Hyperspectral data geometry based estimation of number of endmembers using p-norm based pure pixel identification
-
A. Ambikapathi, T.-H. Chan, C.-Y. Chi, and K. Keizer, Hyperspectral data geometry based estimation of number of endmembers using p-norm based pure pixel identification, IEEE Trans. Geosci. Remote Sensing, 51 (2013), pp. 2753-2769.
-
(2013)
IEEE Trans. Geosci. Remote Sensing
, vol.51
, pp. 2753-2769
-
-
Ambikapathi, A.1
Chan, T.-H.2
Chi, C.-Y.3
Keizer, K.4
-
2
-
-
0035948747
-
The successive projections algorithm for variable selection in spectroscopic multicomponent analysis
-
U.M.C. Araújo, B.T.C. Saldanha, R.K.H. Galvão, T. Yoneyama, H.C. Chame, and V. Visani, The successive projections algorithm for variable selection in spectroscopic multicomponent analysis, Chemometr. Intell. Lab. Syst., 57 (2001), pp. 65-73.
-
(2001)
Chemometr. Intell. Lab. Syst
, vol.57
, pp. 65-73
-
-
Araújo, U.M.C.1
Saldanha, B.T.C.2
Galvão, R.K.H.3
Yoneyama, T.4
Chame, H.C.5
Visani, V.6
-
3
-
-
84897550363
-
A practical algorithm for topic modeling with provable guarantees
-
S. Arora, R. Ge, Y. Halpern, D. Mimno, A. Moitra, D. Sontag, Y. Wu, and M. Zhu, A practical algorithm for topic modeling with provable guarantees, in Proceedings of the International Conference on Machine Learning (ICML '13), Vol. 28, 2013, pp. 280-288.
-
(2013)
Proceedings of the International Conference On Machine Learning (ICML '13)
, vol.28
, pp. 280-288
-
-
Arora, S.1
Ge, R.2
Halpern, Y.3
Mimno, D.4
Moitra, A.5
Sontag, D.6
Wu, Y.7
Zhu, M.8
-
4
-
-
84862609231
-
Computing a nonnegative matrix factorization-provably
-
S. Arora, R. Ge, R. Kannan, and A. Moitra, Computing a nonnegative matrix factorization-provably, in Proceedings of the 44th Annual ACM Symposium on Theory of Computing (STOC '12), 2012, pp. 145-162.
-
(2012)
Proceedings of the 44th Annual ACM Symposium On Theory of Computing (STOC '12)
, pp. 145-162
-
-
Arora, S.1
Ge, R.2
Kannan, R.3
Moitra, A.4
-
5
-
-
84871960604
-
Learning topic models-going beyond SVD
-
S. Arora, R. Ge, and A. Moitra, Learning topic models-going beyond SVD, in Proceedings of the 53rd Annual IEEE Symposium on Foundations of Computer Science (FOCS '12), 2012, pp. 1-10.
-
(2012)
Proceedings of the 53rd Annual IEEE Symposium On Foundations of Computer Science (FOCS '12)
, pp. 1-10
-
-
Arora, S.1
Ge, R.2
Moitra, A.3
-
6
-
-
84861772901
-
Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches
-
J.M. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, and J. Chanussot, Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 5 (2012), pp. 354-379.
-
(2012)
IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens
, vol.5
, pp. 354-379
-
-
Bioucas-Dias, J.M.1
Plaza, A.2
Dobigeon, N.3
Parente, M.4
Du, Q.5
Gader, P.6
Chanussot, J.7
-
7
-
-
84877774066
-
Factoring nonnegative matrices with linear programs
-
V. Bittorf, B. Recht, E. Ré, and J.A. Tropp, Factoring nonnegative matrices with linear programs, in Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS '12), 2012, pp. 1223-1231.
-
(2012)
Proceedings of the 26th Annual Conference On Neural Information Processing Systems (NIPS '12)
, pp. 1223-1231
-
-
Bittorf, V.1
Recht, B.2
Ré, E.3
Tropp, J.A.4
-
8
-
-
80455174042
-
A simplex volume maximization framework for hyperspectral endmember extraction
-
T.-H. Chan, W.-K. Ma, A. Ambikapathi, and C.-Y. Chi, A simplex volume maximization framework for hyperspectral endmember extraction, IEEE Trans. Geosci. Remote Sensing, 49 (2011), pp. 4177-4193.
-
(2011)
IEEE Trans. Geosci. Remote Sensing
, vol.49
, pp. 4177-4193
-
-
Chan, T.-H.1
Ma, W.-K.2
Ambikapathi, A.3
Chi, C.-Y.4
-
9
-
-
54749083549
-
A convex analysis framework for blind separation of non-negative sources
-
T.-H. Chan, W.-K. Ma, C.-Y. Chi, and Y. Wang, A convex analysis framework for blind separation of non-negative sources, IEEE Trans. Signal Process., 56 (2008), pp. 5120-5134.
-
(2008)
IEEE Trans. Signal Process
, vol.56
, pp. 5120-5134
-
-
Chan, T.-H.1
Ma, W.-K.2
Chi, C.-Y.3
Wang, Y.4
-
10
-
-
48249151183
-
Nonnegative matrix factorization: An analytical and interpretive tool in computational biology
-
K. Devarajan, Nonnegative matrix factorization: An analytical and interpretive tool in computational biology, PLoS Comput. Biol., 4 (2008), e1000029.
-
(2008)
PLoS Comput. Biol
, vol.4
-
-
Devarajan, K.1
-
11
-
-
84897526766
-
Topic discovery through data dependent and random projections
-
W. Ding, M.H. Rohban, P. Ishwar, and V. Saligrama, Topic discovery through data dependent and random projections, in Proceedings of the International Conference on Machine Learning (ICML '13), Vol. 28, 2013, pp. 471-479.
-
(2013)
Proceedings of the International Conference On Machine Learning (ICML '13)
, vol.28
, pp. 471-479
-
-
Ding, W.1
Rohban, M.H.2
Ishwar, P.3
Saligrama, V.4
-
12
-
-
84866685721
-
See all by looking at a few: Sparse modeling for finding representative objects
-
E. Elhamifar, G. Sapiro, and R. Vidal, See all by looking at a few: Sparse modeling for finding representative objects, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), 2012, pp. 1600-1607.
-
(2012)
Proceedings of the IEEE Conference On Computer Vision and Pattern Recognition (CVPR '12)
, pp. 1600-1607
-
-
Elhamifar, E.1
Sapiro, G.2
Vidal, R.3
-
13
-
-
84862519707
-
A convex model for nonnegative matrix factorization and dimensionality reduction on physical space
-
E. Esser, M. Moller, S. Osher, G. Sapiro, and J. Xin, A convex model for nonnegative matrix factorization and dimensionality reduction on physical space, IEEE Trans. Image Process., 21 (2012), pp. 3239-3252.
-
(2012)
IEEE Trans. Image Process
, vol.21
, pp. 3239-3252
-
-
Esser, E.1
Moller, M.2
Osher, S.3
Sapiro, G.4
Xin, J.5
-
14
-
-
84901322359
-
Greedy algorithms for pure pixel identification in hyperspectral unmixing: A multiple-measurement vector viewpoint
-
X. Fu, W.-K. Ma, T.-H. Chan, J.M. Bioucas-Dias, and M.-D. Iordache, Greedy algorithms for pure pixel identification in hyperspectral unmixing: A multiple-measurement vector viewpoint, in Proceedings of the European Signal Processing Conference (EUSIPCO '13), 2013.
-
(2013)
Proceedings of the European Signal Processing Conference (EUSIPCO '13)
-
-
Fu, X.1
Ma, W.-K.2
Chan, T.-H.3
Bioucas-Dias, J.M.4
Iordache, M.-D.5
-
15
-
-
84870868704
-
Sparse and unique nonnegative matrix factorization through data preprocessing
-
N. Gillis, Sparse and unique nonnegative matrix factorization through data preprocessing, J. Mach. Learn. Res., 13 (2012), pp. 3349-3386.
-
(2012)
J. Mach. Learn. Res
, vol.13
, pp. 3349-3386
-
-
Gillis, N.1
-
16
-
-
84887361620
-
Robustness analysis of Hottopixx, a linear programming model for factoring nonnegative matrices
-
N. Gillis, Robustness analysis of Hottopixx, a linear programming model for factoring nonnegative matrices, SIAM J. Matrix Anal. Appl., 34 (2013), pp. 1189-1212.
-
(2013)
SIAM J. Matrix Anal. Appl
, vol.34
, pp. 1189-1212
-
-
Gillis, N.1
-
17
-
-
84924200264
-
The why and how of nonnegative matrix factorization
-
J.A.K. Suykens, M. Signoretto, and A. Argyriou, eds., Chapman & Hall/CRC, Boca Raton, FL, to appear (arXiv:1401.5226
-
N. Gillis, The why and how of nonnegative matrix factorization, in Regularization, Optimization, Kernels, and Support Vector Machines, J.A.K. Suykens, M. Signoretto, and A. Argyriou, eds., Chapman & Hall/CRC, Boca Raton, FL, to appear (arXiv:1401.5226).
-
In Regularization, Optimization, Kernels, and Support Vector Machines
-
-
Gillis, N.1
-
18
-
-
84861111031
-
Accelerated multiplicative updates and hierarchical ALS algorithms for nonnegative matrix factorization
-
N. Gillis and F. Glineur, Accelerated multiplicative updates and hierarchical ALS algorithms for nonnegative matrix factorization, Neural Comput., 24 (2012), pp. 1085-1105.
-
(2012)
Neural Comput
, vol.24
, pp. 1085-1105
-
-
Gillis, N.1
Glineur, F.2
-
19
-
-
84901625045
-
Robust near-separable nonnegative matrix factorization using linear optimization
-
N. Gillis and R. Luce, Robust near-separable nonnegative matrix factorization using linear optimization, J. Mach. Learn. Res., 15 (2014), pp. 1249-1280.
-
(2014)
J. Mach. Learn. Res
, vol.15
, pp. 1249-1280
-
-
Gillis, N.1
Luce, R.2
-
21
-
-
84897475291
-
Fast and robust recursive algorithms for separable nonnegative matrix factorization
-
N. Gillis and S.A. Vavasis, Fast and robust recursive algorithms for separable nonnegative matrix factorization, IEEE Trans. Pattern Anal. Mach. Intell., 36 (2014), pp. 698-714.
-
(2014)
IEEE Trans. Pattern Anal. Mach. Intell
, vol.36
, pp. 698-714
-
-
Gillis, N.1
Vavasis, S.A.2
-
22
-
-
0004236492
-
-
3rd ed., The Johns Hopkins University Press, Baltimore, MD
-
G.H. Golub and C.F. Van Loan, Matrix Computations, 3rd ed., The Johns Hopkins University Press, Baltimore, MD, 1996.
-
(1996)
Matrix Computations
-
-
Golub, G.H.1
van Loan, C.F.2
-
25
-
-
84894650699
-
-
A. Kumar, V. Sindhwani, and P. Kambadur, Fast conical hull algorithms for near-separable nonnegative matrix factorization, in Proceedings of the International Conference on Machine Learning (ICML '13), Vol. 28, 2013, pp. 231-239.
-
(2013)
Fast Conical Hull Algorithms For Near-separable Nonnegative Matrix Factorization, In Proceedings of the International Conference On Machine Learning (ICML '13)
, vol.28
, pp. 231-239
-
-
Kumar, A.1
Sindhwani, V.2
Kambadur, P.3
-
26
-
-
0033592606
-
Learning the parts of objects by nonnegative matrix factorization
-
D.D. Lee and H.S. Seung, Learning the parts of objects by nonnegative matrix factorization, Nature, 401 (1999), pp. 788-791.
-
(1999)
Nature
, vol.401
, pp. 788-791
-
-
Lee, D.D.1
Seung, H.S.2
-
27
-
-
85032751209
-
A signal processing perspective on hyperspectral unmixing: Insights from remote sensing
-
W.-K. Ma, J.M. Bioucas-Dias, P. Gader, T.-H. Chan, N. Gillis, A. Plaza, A. Ambikapathi, and C.-Y. Chi, A signal processing perspective on hyperspectral unmixing: Insights from remote sensing, IEEE Signal Process. Mag., 31 (2014), pp. 67-81.
-
(2014)
IEEE Signal Process. Mag
, vol.31
, pp. 67-81
-
-
Ma, W.-K.1
Bioucas-Dias, J.M.2
Gader, P.3
Chan, T.-H.4
Gillis, N.5
Plaza, A.6
Ambikapathi, A.7
Chi, C.-Y.8
-
28
-
-
84899876632
-
Ellipsoidal rounding for nonnegative matrix factorization under noisy separability
-
T. Mizutani, Ellipsoidal rounding for nonnegative matrix factorization under noisy separability, J.Mach. Learn. Res., 15 (2014), pp. 1011-1039.
-
(2014)
J.Mach. Learn. Res
, vol.15
, pp. 1011-1039
-
-
Mizutani, T.1
-
30
-
-
33646682646
-
Nonnegative matrix factorization for spectral data analysis
-
V.P. Pauca, J. Piper, and R.J. Plemmons, Nonnegative matrix factorization for spectral data analysis, Lin. Alg. Appl., 406 (2006), pp. 29-47.
-
(2006)
Lin. Alg. Appl
, vol.406
, pp. 29-47
-
-
Pauca, V.P.1
Piper, J.2
Plemmons, R.J.3
-
31
-
-
1642290713
-
Automatic spectral target recognition in hyperspectral imagery
-
H. Ren and C.-I. Chang, Automatic spectral target recognition in hyperspectral imagery, IEEE Trans. Aero. Electron. Syst., 39 (2003), pp. 1232-1249.
-
(2003)
IEEE Trans. Aero. Electron. Syst
, vol.39
, pp. 1232-1249
-
-
Ren, H.1
Chang, C.-I.2
-
32
-
-
25844488029
-
Document clustering using nonnegative matrix factorization
-
F. Shahnaz, M.W. Berry, V.P. Pauca, and R.J. Plemmons, Document clustering using nonnegative matrix factorization, Inform. Process. Manag., 42 (2006), pp. 373-386.
-
(2006)
Inform. Process. Manag
, vol.42
, pp. 373-386
-
-
Shahnaz, F.1
Berry, M.W.2
Pauca, V.P.3
Plemmons, R.J.4
-
33
-
-
73249153369
-
On the complexity of nonnegative matrix factorization
-
S.A. Vavasis, On the complexity of nonnegative matrix factorization, SIAM J. Optim., 20 (2009), pp. 1364-1377.
-
(2009)
SIAM J. Optim
, vol.20
, pp. 1364-1377
-
-
Vavasis, S.A.1
-
34
-
-
79960845914
-
Community discovery using nonnegative matrix factorization
-
F. Wang, T. Li, X. Wang, S. Zhu, and C. Ding, Community discovery using nonnegative matrix factorization, Data Min. Knowl. Discov., 22 (2011), pp. 493-521.
-
(2011)
Data Min. Knowl. Discov
, vol.22
, pp. 493-521
-
-
Wang, F.1
Li, T.2
Wang, X.3
Zhu, S.4
Ding, C.5
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