en-es
en
0.25
0.5
0.75
1.25
1.5
1.75
2
Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization
Published on Aug 26, 20135595 Views
Nonnegative matrix factorization (NMF) has been shown recently to be tractable under the separability assumption, which amounts for the columns of the input data matrix to belong to the convex cone
Related categories
Chapter list
Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization00:00
Nonnegative Matrix Factorization (NMF)00:08
Text Mining03:01
Hyperspectral Unmixing - 104:35
Hyperspectral Unmixing - 208:38
Hyperspectral Unmixing - 308:45
Can we only solve NMF problems?09:16
Separability Assumption10:42
Is separability a reasonable assumption?11:33
Geometric Interpretation of Separability13:24
Separable NMF14:34
Near-Separable NMF15:23
Near-Separable NMF: Noise and Conditioning - 115:52
Near-Separable NMF: Noise and Conditioning - 216:53
Hottopixx, a Linear Optimization Model - 116:58
An Improved Linear Optimization Model - 121:39
An Improved Linear Optimization Model - 222:03
Hottopixx, a Linear Optimization Model - 222:26
An Improved Linear Optimization Model - 322:32
An Improved Linear Optimization Model - 423:51
Numerical Experiments - 123:52
Numerical Experiments - 224:59
Numerical Experiments - 325:29
Conclusion25:42
Thank you27:04